Polarimetric SAR Data Classification via Reinforcement Learning

被引:1
作者
Wang, Min [1 ]
Wang, Zhiyi [1 ]
Yang, Chen [2 ]
Yang, Shuyuan [2 ]
Gao, Yuteng [3 ]
机构
[1] Xidian Univ, Key Lab Radar Signal Proc, Xian 710071, Shaanxi, Peoples R China
[2] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Shaanxi, Peoples R China
[3] Northwestern Polytech Univ, Elect Engn Dept, Xian 710072, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Polarization synthetic aperture radar (PolSAR); data classification; continuous learning; spatial-polarimetric reward; reinforcement learning; environment; UNSUPERVISED CLASSIFICATION; DECOMPOSITION;
D O I
10.1109/ACCESS.2019.2939232
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Inspired by human's learning characteristic that knowledge is gradually learned little by little, a Spatial-Polarimetric Reinforcement Learning (SPRL) approach is proposed for Polarimetric Synthetic Aperture Radar (PolSAR) data classification, from a new perspective of reinforcement learning. In our method, each pixel has its own "state" and "action", and can modify its "action" based on interactions with the "environment". A spatial-polarimetric "reward" function, is designed from a local neighborhood region to explore both the spatial and polarimetric information for more accurate classification. Thus a self-evolution and model-free classifier can be obtained, which has simple principle and robustness to speckle noises existed in the data. By an interaction with the environment, SPRL can obtain high classification accuracy when only very few labeled pixels are available. Several real PolSAR datasets are used to investigate the effectiveness of the proposed method, and the results show that SPRL is superior to its counterparts.
引用
收藏
页码:137629 / 137637
页数:9
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