Nonlocal patch similarity based heterogeneous remote sensing change detection

Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors make...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sun, Yuli [verfasserIn] Lei, Lin [verfasserIn] Li, Xiao [verfasserIn] Sun, Hao [verfasserIn] Kuang, Gangyao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Unsupervised change detection Heterogeneous data Nonlocal similarity Graph

Übergeordnetes Werk:	Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 109
Übergeordnetes Werk:	volume:109

DOI / URN:	10.1016/j.patcog.2020.107598

Katalog-ID:	ELV004681770

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520			\|a Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method.
650		4	\|a Unsupervised change detection
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650		4	\|a Nonlocal similarity
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allfields	10.1016/j.patcog.2020.107598 doi (DE-627)ELV004681770 (ELSEVIER)S0031-3203(20)30401-5 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Sun, Yuli verfasserin aut Nonlocal patch similarity based heterogeneous remote sensing change detection 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method. Unsupervised change detection Heterogeneous data Nonlocal similarity Graph Lei, Lin verfasserin aut Li, Xiao verfasserin aut Sun, Hao verfasserin aut Kuang, Gangyao verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 109 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 109
spelling	10.1016/j.patcog.2020.107598 doi (DE-627)ELV004681770 (ELSEVIER)S0031-3203(20)30401-5 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Sun, Yuli verfasserin aut Nonlocal patch similarity based heterogeneous remote sensing change detection 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method. Unsupervised change detection Heterogeneous data Nonlocal similarity Graph Lei, Lin verfasserin aut Li, Xiao verfasserin aut Sun, Hao verfasserin aut Kuang, Gangyao verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 109 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 109
allfields_unstemmed	10.1016/j.patcog.2020.107598 doi (DE-627)ELV004681770 (ELSEVIER)S0031-3203(20)30401-5 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Sun, Yuli verfasserin aut Nonlocal patch similarity based heterogeneous remote sensing change detection 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method. Unsupervised change detection Heterogeneous data Nonlocal similarity Graph Lei, Lin verfasserin aut Li, Xiao verfasserin aut Sun, Hao verfasserin aut Kuang, Gangyao verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 109 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 109
allfieldsGer	10.1016/j.patcog.2020.107598 doi (DE-627)ELV004681770 (ELSEVIER)S0031-3203(20)30401-5 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Sun, Yuli verfasserin aut Nonlocal patch similarity based heterogeneous remote sensing change detection 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method. Unsupervised change detection Heterogeneous data Nonlocal similarity Graph Lei, Lin verfasserin aut Li, Xiao verfasserin aut Sun, Hao verfasserin aut Kuang, Gangyao verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 109 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 109
allfieldsSound	10.1016/j.patcog.2020.107598 doi (DE-627)ELV004681770 (ELSEVIER)S0031-3203(20)30401-5 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Sun, Yuli verfasserin aut Nonlocal patch similarity based heterogeneous remote sensing change detection 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method. Unsupervised change detection Heterogeneous data Nonlocal similarity Graph Lei, Lin verfasserin aut Li, Xiao verfasserin aut Sun, Hao verfasserin aut Kuang, Gangyao verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 109 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:109 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 109
language	English
source	Enthalten in Pattern recognition 109 volume:109
sourceStr	Enthalten in Pattern recognition 109 volume:109
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topic_facet	Unsupervised change detection Heterogeneous data Nonlocal similarity Graph
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authorswithroles_txt_mv	Sun, Yuli @@aut@@ Lei, Lin @@aut@@ Li, Xiao @@aut@@ Sun, Hao @@aut@@ Kuang, Gangyao @@aut@@
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title	Nonlocal patch similarity based heterogeneous remote sensing change detection
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title_full	Nonlocal patch similarity based heterogeneous remote sensing change detection
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title_sort	nonlocal patch similarity based heterogeneous remote sensing change detection
title_auth	Nonlocal patch similarity based heterogeneous remote sensing change detection
abstract	Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method.
abstractGer	Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method.
abstract_unstemmed	Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method.
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title_short	Nonlocal patch similarity based heterogeneous remote sensing change detection
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author2	Lei, Lin Li, Xiao Sun, Hao Kuang, Gangyao
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up_date	2024-07-06T23:48:31.666Z
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However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. 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Nonlocal patch similarity based heterogeneous remote sensing change detection

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