DCENet: Diff-Feature Contrast Enhancement Network for Semi-Supervised Hyperspectral Change Detection

Multi-temporal Hyperspectral images (HSIs) have wide applications in change detection (CD) of different land-covers for their rich spectral features and image details. Traditional supervised learning-based HSI CD algorithms often rely on a substantial number of labeled samples. However, it requires a significant cost in sample annotation. In this paper, we propose a diff-feature contrast enhancement network (DCENet) for semi-supervised HSI CD, which leverages a limited number of labeled samples to guide the training process and a large number of unlabeled samples to improve the confidence of change detection. To achieve this, a differential fusion attention (DFA) sub-network is constructed to extract temporal features from the initial input HSI patches. The dual-branch siamese enhancement module (SEM) is utilized to enhance the generalization of differential features in the feature maps. Herein, multi-scale Kullback-Leibler divergence and feature-enhanced probabilistic contrast loss are designed to constrain the SEM. The proposed method excels at detecting subtle changes in bi-temporal HSIs simultaneously improving the generalization performance of networks. The visual and quantitative experimental results on four HSI datasets show that the proposed DCENet outperforms the compared state-of-the-art methods for HSI CD. Codes: https://github.com/Zhoutya/ChangeDetection-DCENet.