PolSAR Marine Aquaculture Detection Based on Nonlocal Stacked Sparse Autoencoder

被引：0

作者：

Fan, Jianchao ^{[1
]}

Liu, Xiaoxin ^{[2
]}

Hu, Yuanyuan ^{[3
]}

Han, Min ^{[3
]}

机构：

[1] Natl Marine Environm Monitoring Ctr, Dept Ocean Remote Sensing, Dalian 116023, Liaoning, Peoples R China

[2] Washington Univ, Comp Sci & Engn, St Louis, MO 63130 USA

[3] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II | 2019年 / 11555卷

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

Polarimetric SAR; Remote sensing images; Nonlocal spatial information; Stacked sparse autoencoder; Classification; IMAGE CLASSIFICATION;

D O I：

10.1007/978-3-030-22808-8_46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：469 / 476

页数：8

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