Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification
Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framewor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1603 |
| _version_ | 1797411926273163264 |
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| author | Xiaoying Wu Xianbin Wen Haixia Xu Liming Yuan Changlun Guo |
| author_facet | Xiaoying Wu Xianbin Wen Haixia Xu Liming Yuan Changlun Guo |
| author_sort | Xiaoying Wu |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results. |
| first_indexed | 2024-03-09T04:53:26Z |
| format | Article |
| id | doaj.art-cec7168eff50492e8fba73d7fe9539b5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:53:26Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cec7168eff50492e8fba73d7fe9539b52023-12-03T13:08:17ZengMDPI AGApplied Sciences2076-34172021-02-01114160310.3390/app11041603Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image ClassificationXiaoying Wu0Xianbin Wen1Haixia Xu2Liming Yuan3Changlun Guo4School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaSynthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.https://www.mdpi.com/2076-3417/11/4/1603multi-featureadaptivekernel combinationSARimage classification |
| spellingShingle | Xiaoying Wu Xianbin Wen Haixia Xu Liming Yuan Changlun Guo Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification Applied Sciences multi-feature adaptive kernel combination SAR image classification |
| title | Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification |
| title_full | Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification |
| title_fullStr | Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification |
| title_full_unstemmed | Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification |
| title_short | Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification |
| title_sort | multi feature fusion and adaptive kernel combination for sar image classification |
| topic | multi-feature adaptive kernel combination SAR image classification |
| url | https://www.mdpi.com/2076-3417/11/4/1603 |
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