极化合成孔径雷达遥感地物分类研究进展

Review of the studies on remote sensing classification based on polarimetric Synthetic Aperture Radar

作为遥感信息提取和分析的重要环节,遥感影像地物分类一直是相关研究领域的热点之一。由于地面目标特性的复杂性和遥感成像手段的多样性,遥感影像的准确分类有赖于对影像特点的深入理解及地物先验知识的充分利用。近些年来,随着合成孔径雷达SAR (Synthetic Aperture Radar),特别是极化SAR技术的进步,SAR遥感地物分类领域的研究有了显著发展。本文旨在对极化SAR遥感地物分类的研究进展进行综述。在介绍SAR遥感基本理论,星载SAR主要数据源的基础上,介绍基于极化分解的分类方法,基于经典机器学习的极化SAR地物分类方法,基于深度学习的极化SAR地物分类方法,融合光学和SAR影像的遥感地物分类方法以及基于紧缩极化SAR的地物分类。然后,介绍极化SAR在海面溢油探测、舰船检测、海岸线提取、土地利用分类、海冰和冰盖分类等地物分类任务上的研究进展。最后,对极化SAR地物分类研究的未来发展进行展望。

Remote sensing technology enables us to monitor the Earth from space and sense the rhythm of rivers, lakes, and seas and the pulse of social and economic development in real time. It also facilitates effective early warning, prevention, and evaluation of natural disasters, in which SAR technology plays an increasingly important role. Remote sensing image classification is an important step of remote sensing image analysis, and it has always been one of the hot spots in related research fields. Owing to the complexity of ground target characteristics and the diversity of remote sensing imaging techniques, the accurate interpretation of remote sensing images requires a deep understanding of the characteristics of the image and fully utilizing the prior knowledge of ground objects. In recent years, the development of Synthetic Aperture Radar(SAR), especially polarimetric SAR technology, has facilitated the rapid growth in the research on remote sensing object classification. In this study, the research progress of polarimetric SAR remote sensing image classification is reviewed. This study firstly introduces the basic theory of SAR remote sensing and the main data sources of spaceborne SAR. Then, it introduces the decomposition of polarimetric SAR data, the classical machine learning algorithms for polarimetric SAR, the deep learning-based algorithms, the methods of fusing optical and SAR images, and the classification algorithms based on compact polarimetric SAR. Next, this study introduces the research progress of polarimetric SAR image classification for marine oil spill detection, ship detection, coastline extraction, land use classification, and sea ice/ice cap classification. Finally, the development trend of polarimetric SAR image classification is prospected. From the perspective of the authors, the development of polarimetric SAR classification has the following trends:(1) from single polarimetric to multi-and compact polarimetric SAR modes;(2) from medium/low resolution, small range to high resolution, large range remote sensing applications;(3) from single temporal to multiple temporal sequence image analysis applications;(4) from manual design of feature extraction methods to automatic feature extraction using deep learning models;(5) from single-source SAR image classification to SAR, optical, LiDAR, and other multi-source image fusion classification. The key technologies of radar signal processing,image analysis, pattern recognition, multi-source information fusion, big data analysis, and other aspects need to be understood to fully utilize the information provided by polarimetric SAR data sources. The rapid development of technology requires talents with interdisciplinary backgrounds such as electronic engineering, remote sensing, and artificial intelligence in this field. The authors hope that through the introduction of this article, readers can improve their understanding of the field of SAR remote sensing classification to a certain extent for better grasping the development trends of this technology.