| 摘要: |
| 极化合成孔径雷达（极化SAR）是当前最先进的遥感监测技术之一。它可以全天时、全天候地进行对地观测，并提供高分辨率、具有丰富地表信息的数据。极化SAR图像分类近年来被广泛研究和应用，而蓬勃发展的深度学习技术大大加速了其进展。基于此现状，本文对深度学习在极化SAR图像分类上的应用进行了综述。综述涵盖了不同类别的深度学习算法，包括监督、无监督、半监督和主动学习算法在此任务上的应用分析。另外，本文分析当前极化SAR图像分类所面临的挑战以及未来的发展趋势。 |
| 关键词: 极化SAR 图像分类 深度学习 |
| DOI：DOI:10.3969/j.issn.1672-2337.2021.05.010 |
| 分类号:TN958 |
| 基金项目:国家自然科学基金 (No.61806162) |
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| A Survey: the Application of Deep Learning in PolSAR Image Classification |
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BI Haixia, WEI Zhiqiang
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1.Faculty of Engineering, University of Bristol, Bristol BS15DD, United Kingdom;2. Xi’an Electronic Engineering Research Institute, Xi’an710100, China
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| Abstract: |
| Polarimetric synthetic aperture radar (PolSAR) is one of the most advanced and important environmental monitoring techniques owing to its all-time and all-weather observation character and strong capability to offer abundant and high-resolution target information. PolSAR image classification has been extensively investigated and applied in recent years. Specifically, the booming of deep learning greatly accelerated the development of PolSAR image classification. This paper provides a survey on the application of deep learning in classifying PolSAR images, where the utilization of different categories of deep learning-based methods, including supervised, unsupervised, semisupervised and active learning approaches are reviewed. In addition, we analyze the current challenges and future trends in this research topic. |
| Key words: survey polarimetric synthetic aperture radar (PolSAR) image classification deep learning |
