Self-Supervised Global–Local Contrastive Learning for Fine-Grained Change Detection in VHR Images
Author:
Affiliation:
1. School of Electronic Engineering, Xidian University, Xi’an, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10021622.pdf?arnumber=10021622
Reference71 articles.
1. ABNet: Adaptive balanced network for multiscale object detection in remote sensing imagery;liu;IEEE Trans Geosci Remote Sens,2021
2. Exploring Cross-Image Pixel Contrast for Semantic Segmentation
3. Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
4. Self-Supervised Change Detection by Fusing SAR and Optical Multi-Temporal Images
5. Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification
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