A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity

Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhu, Wenquan [verfasserIn] Yang, Xinyi [verfasserIn] Liu, Ruoyang [verfasserIn] Zhao, Cenliang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Remote sensing images Texture Feature extraction Spatial arrangement Object recognition

Übergeordnetes Werk:	Enthalten in: International journal of applied earth observation and geoinformation - Amsterdam [u.a.] : Elsevier Science, 1999, 127
Übergeordnetes Werk:	volume:127

DOI / URN:	10.1016/j.jag.2024.103698

Katalog-ID:	ELV067110215

Internformat


LEADER	01000naa a22002652 4500
001	ELV067110215
003	DE-627
005	20240219093029.0
007	cr uuu---uuuuu
008	240219s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.jag.2024.103698 \|2 doi
035			\|a (DE-627)ELV067110215
035			\|a (ELSEVIER)S1569-8432(24)00052-9
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 550 \|q VZ
084			\|a KARTEN \|q DE-1a \|2 fid
084			\|a 38.03 \|2 bkl
084			\|a 74.48 \|2 bkl
084			\|a 74.41 \|2 bkl
100	1		\|a Zhu, Wenquan \|e verfasserin \|0 (orcid)0000-0003-3011-444X \|4 aut
245	1	0	\|a A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
264		1	\|c 2024
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.
650		4	\|a Remote sensing images
650		4	\|a Texture
650		4	\|a Feature extraction
650		4	\|a Spatial arrangement
650		4	\|a Object recognition
700	1		\|a Yang, Xinyi \|e verfasserin \|0 (orcid)0000-0003-1811-8341 \|4 aut
700	1		\|a Liu, Ruoyang \|e verfasserin \|0 (orcid)0000-0001-6526-0370 \|4 aut
700	1		\|a Zhao, Cenliang \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of applied earth observation and geoinformation \|d Amsterdam [u.a.] : Elsevier Science, 1999 \|g 127 \|h Online-Ressource \|w (DE-627)359784119 \|w (DE-600)2097960-5 \|w (DE-576)25927254X \|x 1872-826X \|7 nnns
773	1	8	\|g volume:127
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a FID-KARTEN
912			\|a SSG-OPC-GGO
912			\|a SSG-OPC-AST
912			\|a SSG-OPC-GEO
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
936	b	k	\|a 38.03 \|j Methoden und Techniken der Geowissenschaften \|q VZ
936	b	k	\|a 74.48 \|j Geoinformationssysteme \|q VZ
936	b	k	\|a 74.41 \|j Luftaufnahmen \|j Photogrammetrie \|q VZ
951			\|a AR
952			\|d 127

Indexfelder

author_variant	w z wz x y xy r l rl c z cz
matchkey_str	article:1872826X:2024----::nwetretatoagrtmomauighsailragmnotxuermt
hierarchy_sort_str	2024
bklnumber	38.03 74.48 74.41
publishDate	2024
allfields	10.1016/j.jag.2024.103698 doi (DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Zhu, Wenquan verfasserin (orcid)0000-0003-3011-444X aut A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects. Remote sensing images Texture Feature extraction Spatial arrangement Object recognition Yang, Xinyi verfasserin (orcid)0000-0003-1811-8341 aut Liu, Ruoyang verfasserin (orcid)0000-0001-6526-0370 aut Zhao, Cenliang verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 127 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:127 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 127
spelling	10.1016/j.jag.2024.103698 doi (DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Zhu, Wenquan verfasserin (orcid)0000-0003-3011-444X aut A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects. Remote sensing images Texture Feature extraction Spatial arrangement Object recognition Yang, Xinyi verfasserin (orcid)0000-0003-1811-8341 aut Liu, Ruoyang verfasserin (orcid)0000-0001-6526-0370 aut Zhao, Cenliang verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 127 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:127 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 127
allfields_unstemmed	10.1016/j.jag.2024.103698 doi (DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Zhu, Wenquan verfasserin (orcid)0000-0003-3011-444X aut A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects. Remote sensing images Texture Feature extraction Spatial arrangement Object recognition Yang, Xinyi verfasserin (orcid)0000-0003-1811-8341 aut Liu, Ruoyang verfasserin (orcid)0000-0001-6526-0370 aut Zhao, Cenliang verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 127 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:127 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 127
allfieldsGer	10.1016/j.jag.2024.103698 doi (DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Zhu, Wenquan verfasserin (orcid)0000-0003-3011-444X aut A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects. Remote sensing images Texture Feature extraction Spatial arrangement Object recognition Yang, Xinyi verfasserin (orcid)0000-0003-1811-8341 aut Liu, Ruoyang verfasserin (orcid)0000-0001-6526-0370 aut Zhao, Cenliang verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 127 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:127 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 127
allfieldsSound	10.1016/j.jag.2024.103698 doi (DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Zhu, Wenquan verfasserin (orcid)0000-0003-3011-444X aut A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects. Remote sensing images Texture Feature extraction Spatial arrangement Object recognition Yang, Xinyi verfasserin (orcid)0000-0003-1811-8341 aut Liu, Ruoyang verfasserin (orcid)0000-0001-6526-0370 aut Zhao, Cenliang verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 127 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:127 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 127
language	English
source	Enthalten in International journal of applied earth observation and geoinformation 127 volume:127
sourceStr	Enthalten in International journal of applied earth observation and geoinformation 127 volume:127
format_phy_str_mv	Article
bklname	Methoden und Techniken der Geowissenschaften Geoinformationssysteme Luftaufnahmen Photogrammetrie
institution	findex.gbv.de
topic_facet	Remote sensing images Texture Feature extraction Spatial arrangement Object recognition
dewey-raw	550
isfreeaccess_bool	false
container_title	International journal of applied earth observation and geoinformation
authorswithroles_txt_mv	Zhu, Wenquan @@aut@@ Yang, Xinyi @@aut@@ Liu, Ruoyang @@aut@@ Zhao, Cenliang @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	359784119
dewey-sort	3550
id	ELV067110215
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV067110215</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240219093029.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240219s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jag.2024.103698</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV067110215</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1569-8432(24)00052-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">KARTEN</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.41</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhu, Wenquan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3011-444X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing images</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial arrangement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object recognition</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Xinyi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1811-8341</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Ruoyang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6526-0370</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Cenliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of applied earth observation and geoinformation</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1999</subfield><subfield code="g">127</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)359784119</subfield><subfield code="w">(DE-600)2097960-5</subfield><subfield code="w">(DE-576)25927254X</subfield><subfield code="x">1872-826X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:127</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-KARTEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">38.03</subfield><subfield code="j">Methoden und Techniken der Geowissenschaften</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.48</subfield><subfield code="j">Geoinformationssysteme</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.41</subfield><subfield code="j">Luftaufnahmen</subfield><subfield code="j">Photogrammetrie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">127</subfield></datafield></record></collection>
author	Zhu, Wenquan
spellingShingle	Zhu, Wenquan ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Remote sensing images misc Texture misc Feature extraction misc Spatial arrangement misc Object recognition A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
authorStr	Zhu, Wenquan
ppnlink_with_tag_str_mv	@@773@@(DE-627)359784119
format	electronic Article
dewey-ones	550 - Earth sciences
delete_txt_mv	keep
author_role	aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	1872-826X
topic_title	550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity Remote sensing images Texture Feature extraction Spatial arrangement Object recognition
topic	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Remote sensing images misc Texture misc Feature extraction misc Spatial arrangement misc Object recognition
topic_unstemmed	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Remote sensing images misc Texture misc Feature extraction misc Spatial arrangement misc Object recognition
topic_browse	ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Remote sensing images misc Texture misc Feature extraction misc Spatial arrangement misc Object recognition
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International journal of applied earth observation and geoinformation
hierarchy_parent_id	359784119
dewey-tens	550 - Earth sciences & geology
hierarchy_top_title	International journal of applied earth observation and geoinformation
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X
title	A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
ctrlnum	(DE-627)ELV067110215 (ELSEVIER)S1569-8432(24)00052-9
title_full	A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
author_sort	Zhu, Wenquan
journal	International journal of applied earth observation and geoinformation
journalStr	International journal of applied earth observation and geoinformation
lang_code	eng
isOA_bool	false
dewey-hundreds	500 - Science
recordtype	marc
publishDateSort	2024
contenttype_str_mv	zzz
author_browse	Zhu, Wenquan Yang, Xinyi Liu, Ruoyang Zhao, Cenliang
container_volume	127
class	550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl
format_se	Elektronische Aufsätze
author-letter	Zhu, Wenquan
doi_str_mv	10.1016/j.jag.2024.103698
normlink	(ORCID)0000-0003-3011-444X (ORCID)0000-0003-1811-8341 (ORCID)0000-0001-6526-0370
normlink_prefix_str_mv	(orcid)0000-0003-3011-444X (orcid)0000-0003-1811-8341 (orcid)0000-0001-6526-0370
dewey-full	550
author2-role	verfasserin
title_sort	a new feature extraction algorithm for measuring the spatial arrangement of texture primitives: distance coding diversity
title_auth	A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
abstract	Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.
abstractGer	Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.
abstract_unstemmed	Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity
remote_bool	true
author2	Yang, Xinyi Liu, Ruoyang Zhao, Cenliang
author2Str	Yang, Xinyi Liu, Ruoyang Zhao, Cenliang
ppnlink	359784119
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.jag.2024.103698
up_date	2024-07-06T20:07:46.738Z
_version_	1803861587908362240
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV067110215</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240219093029.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240219s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jag.2024.103698</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV067110215</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1569-8432(24)00052-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">KARTEN</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.41</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhu, Wenquan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3011-444X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing images</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial arrangement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object recognition</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Xinyi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1811-8341</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Ruoyang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6526-0370</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Cenliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of applied earth observation and geoinformation</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1999</subfield><subfield code="g">127</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)359784119</subfield><subfield code="w">(DE-600)2097960-5</subfield><subfield code="w">(DE-576)25927254X</subfield><subfield code="x">1872-826X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:127</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-KARTEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">38.03</subfield><subfield code="j">Methoden und Techniken der Geowissenschaften</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.48</subfield><subfield code="j">Geoinformationssysteme</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.41</subfield><subfield code="j">Luftaufnahmen</subfield><subfield code="j">Photogrammetrie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">127</subfield></datafield></record></collection>
score	7.402524

Nicht das Richtige dabei?

Schreiben Sie uns!

A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?