CNN-Improved Superpixel-to-Pixel Fuzzy Graph Convolution Network for PolSAR Image Classification-Reference-Cited by-同舟云学术

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

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Shi Junfei¹^ORCID,He Tiansheng¹,Ji Shanshan¹,Nie Mengmeng¹,Jin Haiyan¹^ORCID

Affiliation:

1. Department of Computer Science and Technology, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an, China

Funder

National Natural Science Foundation of China

Science and Technology Program of Beilin District in Xi’an

Youth Innovation Team Research Program Project of Shaanxi Provincial Education Department

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10292882.pdf?arnumber=10292882

Reference48 articles.

1. An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data

2. Adaptive Graph Convolutional Network for PolSAR Image Classification

3. An automatic K-Wishart distribution ship detector for PolSAR data

4. Deep Fuzzy Graph Convolutional Networks for PolSAR Imagery Pixelwise Classification

5. Automated non-Gaussian clustering of polarimetric SAR;doulgeris;Proc 8th Eur Conf Synth Aperture Radar,2010

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