A Research on Input Schemes of Polarimetric SAR Classification Through Deep Learning-Reference-Cited by-同舟云学术

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A Research on Input Schemes of Polarimetric SAR Classification Through Deep Learning

Published:2024-07-07 Issue: Volume: Page:11115-11118
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Zhang Shuaiying¹,An Wentao²,Zhang Yue³,Dong Zhen¹

Affiliation:

1. National University of Defense Technology (NUDT),College of Electronic Science and Engineering,Changsha,China,410073

2. National Satellite Ocean Application Service,Beijing,China,100081

3. Ocean University of China,Faculty of Information Science and Engineering,Qingdao,China,266100

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641105.pdf?arnumber=10641105

Reference12 articles.

1. POLSAR Image Analysis of Wetlands Using a Modified Four-Component Scattering Power Decomposition

2. CNN-Improved Superpixel-to-Pixel Fuzzy Graph Convolution Network for PolSAR Image Classification

3. PolSAR Ship Detection Based on Noncircularity and Oblique Subspace Projection

4. A Fine PolSAR Terrain Classification Algorithm Using the Texture Feature Fusion Based Improved Convolutional Autoencoder

5. Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks

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