A Graph-Based Hyperspectral Change Detection Framework Using Difference Augmentation and Progressive Reconstruction With Limited Labels

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 14
• Main Authors: Yang, Bin, Cheng, Xinwei, Chen, Wei, Ye, Xin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Change detection; Change detection (CD); Data mining; difference augmentation; Feature extraction; graph convolutional network (GCN); hyperspectral images (HSIs); Hyperspectral imaging; Image reconstruction; Labels; Land cover; limited labels; Modules; progressive reconstruction; Reconstruction; Remote sensing; Representations; Research hot spots; Task analysis; Training; Transformers

Identifying land cover changes based on hyperspectral images (HSIs) has been a research hotspot in the field of remote sensing (RS). In recent years, deep-learning (DL)-based change detection (CD) methods have advanced the development of this subject due to their powerful feature representation capa...