深度学习在极化SAR图像分类上的应用综述

A Survey:the Application of Deep Learning in PolSAR Image Classification

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。

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.