PolSAR Marine Aquaculture Detection Based on Nonlocal Stacked Sparse Autoencoder

Jianchao Fan, Xiaoxin Liu, Yuanyuan Hu, Min Han

Abstract: Marine aquaculture plays an important role in marine economic, which distributes widely around the coast. Using satellite remote sensing monitoring, it can achieve large scale dynamic monitoring. As a classic model of deep learning, stacked sparse autoencoder (SSAE) has the advantages of simple model and self-learning of features. Nonlocal spatial information is utilized to assist SSAE construct NSSAE to improve the precision in this paper. Experimental results demonstrate the superiority of nonlocal SSAE methods on marine target recognition.