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Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems

  • Published:2017-09 Issue: Volume:255 Page:52-60
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

Gomez LuisORCID,Alvarez Luis,Mazorra Luis,Frery Alejandro C.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference23 articles.

1. Remote Sensing Digital Image Analysis;Richards,2006

2. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images;Geman;IEEE Trans. Pattern Anal. Mach. Intell.,1984

3. Classification of PolSAR imagery by solving a diffusion-reaction system;Gomez,2015

4. Classification of complex Wishart matrices with a diffusion-reaction system guided by stochastic distances;Gomez;Philosophical Trans. R. Soc. A: Math. Phys. Eng. Sci.,2015

5. Polarimetric Radar Imaging: From Basics to Applications;Lee,2009

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1. Feature-preserving synthesis of termite-mimetic spinodal nest morphology;iScience;2024-01

2. A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images;IET Radar, Sonar & Navigation;2023-10-10

3. Self-Supervised Deep Network for Automatic Target Recognition in SAR;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. Bibliography;SAR Image Analysis – A Computational Statistics Approach;2022-07-19

5. Comparing the Gambini's Algorithm and a CFAR Method to Edge Detection for Polsar Images;IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium;2022-07-17
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