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.
J. Fan—The work described in the paper was supported by the National Key R&D Program of China (2017YFC1404902, 2016YFC1401007); National Natural Science Foundation of China (41706195, 61773087); National High Resolution Special Research (41-Y30B12-9001-14/16); Key Laboratory of Sea-Area Management Technology Foundation (201701).
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Fan, J., Liu, X., Hu, Y., Han, M. (2019). PolSAR Marine Aquaculture Detection Based on Nonlocal Stacked Sparse Autoencoder. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_46
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