Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes...
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| Format: | Article |
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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/2072-4292/17/15/2663 |
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| author | Nana Jiang Wenbo Zhao Jiao Guo Qiang Zhao Jubo Zhu |
| author_facet | Nana Jiang Wenbo Zhao Jiao Guo Qiang Zhao Jubo Zhu |
| author_sort | Nana Jiang |
| collection | DOAJ |
| description | Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. |
| format | Article |
| id | doaj-art-ba916ee702a845c1a3afaec486900704 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ba916ee702a845c1a3afaec4869007042025-08-20T03:36:22ZengMDPI AGRemote Sensing2072-42922025-08-011715266310.3390/rs17152663Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image ClassificationNana Jiang0Wenbo Zhao1Jiao Guo2Qiang Zhao3Jubo Zhu4School of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519082, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaShanghai Institute of Satellite Engineering, Shanghai 201109, ChinaSchool of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519082, ChinaCompared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness.https://www.mdpi.com/2072-4292/17/15/2663complex-valued networkcomplex-valued 3D convolutionmulti-scale featurepolarimetric synthetic aperture radar (PolSAR) image classification |
| spellingShingle | Nana Jiang Wenbo Zhao Jiao Guo Qiang Zhao Jubo Zhu Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification Remote Sensing complex-valued network complex-valued 3D convolution multi-scale feature polarimetric synthetic aperture radar (PolSAR) image classification |
| title | Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification |
| title_full | Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification |
| title_fullStr | Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification |
| title_full_unstemmed | Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification |
| title_short | Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification |
| title_sort | multi scale feature extraction with 3d complex valued network for polsar image classification |
| topic | complex-valued network complex-valued 3D convolution multi-scale feature polarimetric synthetic aperture radar (PolSAR) image classification |
| url | https://www.mdpi.com/2072-4292/17/15/2663 |
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