Distance Measures of Polarimetric SAR Image Data: A Survey
Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarize...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5873 |
| _version_ | 1797464006351388672 |
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| author | Xianxiang Qin Yanning Zhang Ying Li Yinglei Cheng Wangsheng Yu Peng Wang Huanxin Zou |
| author_facet | Xianxiang Qin Yanning Zhang Ying Li Yinglei Cheng Wangsheng Yu Peng Wang Huanxin Zou |
| author_sort | Xianxiang Qin |
| collection | DOAJ |
| description | Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields. |
| first_indexed | 2024-03-09T18:01:48Z |
| format | Article |
| id | doaj.art-363e9d864fcc4d86946b083bb3816a94 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:01:48Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-363e9d864fcc4d86946b083bb3816a942023-11-24T09:51:51ZengMDPI AGRemote Sensing2072-42922022-11-011422587310.3390/rs14225873Distance Measures of Polarimetric SAR Image Data: A SurveyXianxiang Qin0Yanning Zhang1Ying Li2Yinglei Cheng3Wangsheng Yu4Peng Wang5Huanxin Zou6National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaNational Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaNational Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaDistance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.https://www.mdpi.com/2072-4292/14/22/5873PolSAR imagenorm distancesgeodesic distancesmaximum likelihood distancesGLRT distancesstochastic distances |
| spellingShingle | Xianxiang Qin Yanning Zhang Ying Li Yinglei Cheng Wangsheng Yu Peng Wang Huanxin Zou Distance Measures of Polarimetric SAR Image Data: A Survey Remote Sensing PolSAR image norm distances geodesic distances maximum likelihood distances GLRT distances stochastic distances |
| title | Distance Measures of Polarimetric SAR Image Data: A Survey |
| title_full | Distance Measures of Polarimetric SAR Image Data: A Survey |
| title_fullStr | Distance Measures of Polarimetric SAR Image Data: A Survey |
| title_full_unstemmed | Distance Measures of Polarimetric SAR Image Data: A Survey |
| title_short | Distance Measures of Polarimetric SAR Image Data: A Survey |
| title_sort | distance measures of polarimetric sar image data a survey |
| topic | PolSAR image norm distances geodesic distances maximum likelihood distances GLRT distances stochastic distances |
| url | https://www.mdpi.com/2072-4292/14/22/5873 |
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