Polarimetric sar data classification via reinforcement learning

Abstract

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.