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

Abstract

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.