Feature Matching Improvement through Merging Features for Remote Sensing Imagery

Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We...
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Gespeichert in:

Autor*in:	Karim, Shahid [verfasserIn] Zhang, Ye Brohi, Ali Anwar Asif, Muhammad Rizwan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Feature matching SURF FAST BRISK RANSAC

Anmerkung:	© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Übergeordnetes Werk:	Enthalten in: 3D Research - Berlin : Springer, 2010, 9(2018), 4 vom: 17. Okt.
Übergeordnetes Werk:	volume:9 ; year:2018 ; number:4 ; day:17 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s13319-018-0203-x

Katalog-ID:	SPR031330479

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520			\|a Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time.
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spelling	10.1007/s13319-018-0203-x doi (DE-627)SPR031330479 (SPR)s13319-018-0203-x-e DE-627 ger DE-627 rakwb eng Karim, Shahid verfasserin aut Feature Matching Improvement through Merging Features for Remote Sensing Imagery 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. Feature matching (dpeaa)DE-He213 SURF (dpeaa)DE-He213 FAST (dpeaa)DE-He213 BRISK (dpeaa)DE-He213 RANSAC (dpeaa)DE-He213 Zhang, Ye aut Brohi, Ali Anwar aut Asif, Muhammad Rizwan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 4 vom: 17. Okt. (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:4 day:17 month:10 https://dx.doi.org/10.1007/s13319-018-0203-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 4 17 10
allfields_unstemmed	10.1007/s13319-018-0203-x doi (DE-627)SPR031330479 (SPR)s13319-018-0203-x-e DE-627 ger DE-627 rakwb eng Karim, Shahid verfasserin aut Feature Matching Improvement through Merging Features for Remote Sensing Imagery 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. Feature matching (dpeaa)DE-He213 SURF (dpeaa)DE-He213 FAST (dpeaa)DE-He213 BRISK (dpeaa)DE-He213 RANSAC (dpeaa)DE-He213 Zhang, Ye aut Brohi, Ali Anwar aut Asif, Muhammad Rizwan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 4 vom: 17. Okt. (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:4 day:17 month:10 https://dx.doi.org/10.1007/s13319-018-0203-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 4 17 10
allfieldsGer	10.1007/s13319-018-0203-x doi (DE-627)SPR031330479 (SPR)s13319-018-0203-x-e DE-627 ger DE-627 rakwb eng Karim, Shahid verfasserin aut Feature Matching Improvement through Merging Features for Remote Sensing Imagery 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. Feature matching (dpeaa)DE-He213 SURF (dpeaa)DE-He213 FAST (dpeaa)DE-He213 BRISK (dpeaa)DE-He213 RANSAC (dpeaa)DE-He213 Zhang, Ye aut Brohi, Ali Anwar aut Asif, Muhammad Rizwan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 4 vom: 17. Okt. (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:4 day:17 month:10 https://dx.doi.org/10.1007/s13319-018-0203-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 4 17 10
allfieldsSound	10.1007/s13319-018-0203-x doi (DE-627)SPR031330479 (SPR)s13319-018-0203-x-e DE-627 ger DE-627 rakwb eng Karim, Shahid verfasserin aut Feature Matching Improvement through Merging Features for Remote Sensing Imagery 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. Feature matching (dpeaa)DE-He213 SURF (dpeaa)DE-He213 FAST (dpeaa)DE-He213 BRISK (dpeaa)DE-He213 RANSAC (dpeaa)DE-He213 Zhang, Ye aut Brohi, Ali Anwar aut Asif, Muhammad Rizwan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 4 vom: 17. Okt. (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:4 day:17 month:10 https://dx.doi.org/10.1007/s13319-018-0203-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 4 17 10
language	English
source	Enthalten in 3D Research 9(2018), 4 vom: 17. Okt. volume:9 year:2018 number:4 day:17 month:10
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authorswithroles_txt_mv	Karim, Shahid @@aut@@ Zhang, Ye @@aut@@ Brohi, Ali Anwar @@aut@@ Asif, Muhammad Rizwan @@aut@@
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author	Karim, Shahid
spellingShingle	Karim, Shahid misc Feature matching misc SURF misc FAST misc BRISK misc RANSAC Feature Matching Improvement through Merging Features for Remote Sensing Imagery
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topic_title	Feature Matching Improvement through Merging Features for Remote Sensing Imagery Feature matching (dpeaa)DE-He213 SURF (dpeaa)DE-He213 FAST (dpeaa)DE-He213 BRISK (dpeaa)DE-He213 RANSAC (dpeaa)DE-He213
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title	Feature Matching Improvement through Merging Features for Remote Sensing Imagery
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title_sort	feature matching improvement through merging features for remote sensing imagery
title_auth	Feature Matching Improvement through Merging Features for Remote Sensing Imagery
abstract	Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstractGer	Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstract_unstemmed	Abstract Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time. © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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title_short	Feature Matching Improvement through Merging Features for Remote Sensing Imagery
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