Sparse Feature Clustering Network for Unsupervised SAR Image Change Detection

Author:

Zhang Wenhua1ORCID,Jiao Licheng2ORCID,Liu Fang3ORCID,Yang Shuyuan3ORCID,Song Wei3,Liu Jia2ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China

3. School of Artificial Intelligence, Xidian University, Xi’an, China

Funder

Project through the Foundation for Innovative Research Groups of the National Natural Science Foundation of China

National Natural Science Foundation of China

Fund for Foreign Scholars in University Research and Teaching Program’s 111 Project

Major Research Plan of the National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province, China

Open Research Fund in 2021 of Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense

Fundamental Research Funds for the Central Universities

Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Reference66 articles.

1. Efficient learning of sparse representations with an energy-based model;ranzato;Proc Adv Neural Inf Process Syst,2006

2. SAR Image Filtering Via Learned Dictionaries and Sparse Representations

3. A Supervised Artificial Immune Classifier for Remote-Sensing Imagery

4. Speckle noise suppression of SAR image using hybrid order statistics filters;shanthi;Int J Advanced Sci Technol,2011

5. Unsupervised remote sensing image classification using an artificial immune network

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