Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22808-8_46 | ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II |
Keywords | Field | DocType |
Polarimetric SAR, Remote sensing images, Nonlocal spatial information, Stacked sparse autoencoder, Classification | Spatial analysis,Autoencoder,Pattern recognition,Computer science,Satellite remote sensing,Polarimetric sar,Artificial intelligence,Deep learning | Conference |
Volume | ISSN | Citations |
11555 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianchao Fan | 1 | 186 | 15.72 |
| Xiaoxin Liu | 2 | 24 | 2.53 |
| Yuanyuan Hu | 3 | 0 | 1.01 |
| Min Han | 4 | 761 | 68.01 |