PolSAR Marine Aquaculture Detection Based on Nonlocal Stacked Sparse Autoencoder

  • Jianchao FanEmail author
  • Xiaoxin Liu
  • Yuanyuan Hu
  • Min Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)


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.


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


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jianchao Fan
    • 1
    Email author
  • Xiaoxin Liu
    • 2
  • Yuanyuan Hu
    • 3
  • Min Han
    • 3
  1. 1.Department of Ocean Remote SensingNational Marine Environmental Monitoring CenterDalianChina
  2. 2.Computer Science and EngineeringWashington University in St. LouisSaint LouisUSA
  3. 3.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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