PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network
Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an exte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2723 |
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| author | Mengxuan Zhang Jingyuan Shi Long Liu Wenbo Zhang Jie Feng Jin Zhu Boce Chu |
| author_facet | Mengxuan Zhang Jingyuan Shi Long Liu Wenbo Zhang Jie Feng Jin Zhu Boce Chu |
| author_sort | Mengxuan Zhang |
| collection | DOAJ |
| description | Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. They do not make full use of polarimetric information and do not necessarily lead to good enough superpixels. The deep learning methods can extract effective deep features but they are difficult to combine with superpixel generation to achieve true end-to-end training. Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. PolSAR superpixels can be generated based on deep polarization feature extraction and need no traditional clustering process. Both the performance and efficiency of generations of PolSAR superpixels can be enhanced effectively. The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. It can achieve excellent classification performance by connecting a very simple classification network, which is helpful to improve the efficiency of the subsequent PolSAR image classification tasks. |
| format | Article |
| id | doaj-art-35be7a3051334b2f83367a674cc34be0 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-35be7a3051334b2f83367a674cc34be02025-08-20T03:36:30ZengMDPI AGRemote Sensing2072-42922025-08-011715272310.3390/rs17152723PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation NetworkMengxuan Zhang0Jingyuan Shi1Long Liu2Wenbo Zhang3Jie Feng4Jin Zhu5Boce Chu6Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, ChinaSchool of Engineering, Xidian University, Xi’an 710071, ChinaSchool of Engineering, Xidian University, Xi’an 710071, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, China54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, ChinaPolarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. They do not make full use of polarimetric information and do not necessarily lead to good enough superpixels. The deep learning methods can extract effective deep features but they are difficult to combine with superpixel generation to achieve true end-to-end training. Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. PolSAR superpixels can be generated based on deep polarization feature extraction and need no traditional clustering process. Both the performance and efficiency of generations of PolSAR superpixels can be enhanced effectively. The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. It can achieve excellent classification performance by connecting a very simple classification network, which is helpful to improve the efficiency of the subsequent PolSAR image classification tasks.https://www.mdpi.com/2072-4292/17/15/2723superpixel generationend-to-endfully convolutional networkpolarimetric informationPolarimetric Synthetic Aperture Radar image classification |
| spellingShingle | Mengxuan Zhang Jingyuan Shi Long Liu Wenbo Zhang Jie Feng Jin Zhu Boce Chu PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network Remote Sensing superpixel generation end-to-end fully convolutional network polarimetric information Polarimetric Synthetic Aperture Radar image classification |
| title | PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network |
| title_full | PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network |
| title_fullStr | PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network |
| title_full_unstemmed | PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network |
| title_short | PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network |
| title_sort | polsar sfcgn an end to end polsar superpixel fully convolutional generation network |
| topic | superpixel generation end-to-end fully convolutional network polarimetric information Polarimetric Synthetic Aperture Radar image classification |
| url | https://www.mdpi.com/2072-4292/17/15/2723 |
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