An Intra-Class Ranking Metric for Remote Sensing Image Retrieval

With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variabili...
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Gespeichert in:

Autor*in:	Pingping Liu [verfasserIn] Xiaofeng Liu [verfasserIn] Yifan Wang [verfasserIn] Zetong Liu [verfasserIn] Qiuzhan Zhou [verfasserIn] Qingliang Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	deep metric learning loss function image retrieval self-supervised learning sample generation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 16, p 3943
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:16, p 3943

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15163943

Katalog-ID:	DOAJ093558503

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allfields	10.3390/rs15163943 doi (DE-627)DOAJ093558503 (DE-599)DOAJa788afda16c341e98077be1e4d3a5bdb DE-627 ger DE-627 rakwb eng Pingping Liu verfasserin aut An Intra-Class Ranking Metric for Remote Sensing Image Retrieval 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval. deep metric learning loss function image retrieval self-supervised learning sample generation Science Q Xiaofeng Liu verfasserin aut Yifan Wang verfasserin aut Zetong Liu verfasserin aut Qiuzhan Zhou verfasserin aut Qingliang Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 3943 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 3943 https://doi.org/10.3390/rs15163943 kostenfrei https://doaj.org/article/a788afda16c341e98077be1e4d3a5bdb kostenfrei https://www.mdpi.com/2072-4292/15/16/3943 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 16, p 3943
spelling	10.3390/rs15163943 doi (DE-627)DOAJ093558503 (DE-599)DOAJa788afda16c341e98077be1e4d3a5bdb DE-627 ger DE-627 rakwb eng Pingping Liu verfasserin aut An Intra-Class Ranking Metric for Remote Sensing Image Retrieval 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval. deep metric learning loss function image retrieval self-supervised learning sample generation Science Q Xiaofeng Liu verfasserin aut Yifan Wang verfasserin aut Zetong Liu verfasserin aut Qiuzhan Zhou verfasserin aut Qingliang Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 3943 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 3943 https://doi.org/10.3390/rs15163943 kostenfrei https://doaj.org/article/a788afda16c341e98077be1e4d3a5bdb kostenfrei https://www.mdpi.com/2072-4292/15/16/3943 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 16, p 3943
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allfieldsGer	10.3390/rs15163943 doi (DE-627)DOAJ093558503 (DE-599)DOAJa788afda16c341e98077be1e4d3a5bdb DE-627 ger DE-627 rakwb eng Pingping Liu verfasserin aut An Intra-Class Ranking Metric for Remote Sensing Image Retrieval 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval. deep metric learning loss function image retrieval self-supervised learning sample generation Science Q Xiaofeng Liu verfasserin aut Yifan Wang verfasserin aut Zetong Liu verfasserin aut Qiuzhan Zhou verfasserin aut Qingliang Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 3943 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 3943 https://doi.org/10.3390/rs15163943 kostenfrei https://doaj.org/article/a788afda16c341e98077be1e4d3a5bdb kostenfrei https://www.mdpi.com/2072-4292/15/16/3943 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 16, p 3943
allfieldsSound	10.3390/rs15163943 doi (DE-627)DOAJ093558503 (DE-599)DOAJa788afda16c341e98077be1e4d3a5bdb DE-627 ger DE-627 rakwb eng Pingping Liu verfasserin aut An Intra-Class Ranking Metric for Remote Sensing Image Retrieval 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval. deep metric learning loss function image retrieval self-supervised learning sample generation Science Q Xiaofeng Liu verfasserin aut Yifan Wang verfasserin aut Zetong Liu verfasserin aut Qiuzhan Zhou verfasserin aut Qingliang Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 3943 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 3943 https://doi.org/10.3390/rs15163943 kostenfrei https://doaj.org/article/a788afda16c341e98077be1e4d3a5bdb kostenfrei https://www.mdpi.com/2072-4292/15/16/3943 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 16, p 3943
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author	Pingping Liu
spellingShingle	Pingping Liu misc deep metric learning misc loss function misc image retrieval misc self-supervised learning misc sample generation misc Science misc Q An Intra-Class Ranking Metric for Remote Sensing Image Retrieval
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topic_title	An Intra-Class Ranking Metric for Remote Sensing Image Retrieval deep metric learning loss function image retrieval self-supervised learning sample generation
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title	An Intra-Class Ranking Metric for Remote Sensing Image Retrieval
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title_sort	intra-class ranking metric for remote sensing image retrieval
title_auth	An Intra-Class Ranking Metric for Remote Sensing Image Retrieval
abstract	With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval.
abstractGer	With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval.
abstract_unstemmed	With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAPK, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval.
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title_short	An Intra-Class Ranking Metric for Remote Sensing Image Retrieval
url	https://doi.org/10.3390/rs15163943 https://doaj.org/article/a788afda16c341e98077be1e4d3a5bdb https://www.mdpi.com/2072-4292/15/16/3943 https://doaj.org/toc/2072-4292
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author2	Xiaofeng Liu Yifan Wang Zetong Liu Qiuzhan Zhou Qingliang Li
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up_date	2024-07-03T18:02:30.267Z
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An Intra-Class Ranking Metric for Remote Sensing Image Retrieval

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