Unsupervised change detection in PolSAR images using siamese encoder–decoder framework based on graph-context attention network

Author:

Yang Zhifei,Wu Yan,Li Ming,Hu Xin,Li Zhikang

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference33 articles.

1. Polarimetric SAR change detection with the complex hotelling–lawley trace statistic;Akbari;IEEE Trans. Geosci. Remote Sens.,2016

2. Relational inductive biases, deep learning, and graph networks;Battaglia,2018

3. Change detection in multilook polarimetric SAR imagery with determinant ratio test statistic;Bouhlel;IEEE Trans. Geosci. Remote Sens.,2020

4. Multilook polarimetric SAR change detection using stochastic distances between matrix-variate Gd0 distributions;Bouhlel;IEEE Trans. Geosci. Remote Sens.,2020

5. A test statistic in the complex wishart distribution and its application to change detection in polarimetric SAR data;Conradsen;IEEE Trans. Geosci. Remote Sens.,2003

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1. Change detection of multisource remote sensing images: a review;International Journal of Digital Earth;2024-09-09

2. Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China);International Journal of Applied Earth Observation and Geoinformation;2024-03

3. Transfer Adaptation Learning for Target Recognition in SAR Images: A Survey;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

4. Unsupervised Difference-Guided Adaptive Fusion Network for Change Detection in PolSAR Images;IEEE Transactions on Geoscience and Remote Sensing;2024

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