上海航天2018，Vol.35Issue(3)：1-7,7.DOI:10.19328/j.cnki.1006-1630.2018.03.001
基于深度学习算法的极化合成孔径雷达通用分类器设计
General Purpose PolSAR Classifier Based on Deep Learning Algorithm
摘要
Abstract
Terrain classification is one of the most important applications of polarimetric synthetic aperture radar (PolSAR)data. The classic algorithms are limited by manual designed features and classifiers. However, deep learning can extract hierarchical features from big data. Open literatures of deep-learning based PolSAR data classification approaches are firstly reviewed, and one general purpose PolSAR image classifier is then presented based on deep learning and PolSAR big data. Manually labelled data are used for training, and experiments are carried out on both airborne and space-borne SAR data with variant resolution. The results show that the proposed classifier is highly accurate and efficient, which is helpful for big data utilization, especially for GF-3PolSAR data.关键词
合成孔径雷达/极化/深度学习/卷积神经网络/地物分类Key words
synthetic aperture radar(SAR)/polarimetric/deep learning/convolutional neural network(CNN)/terrain classification分类
信息技术与安全科学引用本文复制引用
李索,张支勉,王海鹏..基于深度学习算法的极化合成孔径雷达通用分类器设计[J].上海航天,2018,35(3):1-7,7.基金项目
国家自然科学基金(61571132,61331020) （61571132,61331020）
上海航天科技创新基金(SAST2016061) （SAST2016061）
