AIWSEN: Adaptive Information Weighting and Synchronized Enhancement Network for Hyperspectral Change Detection

Hyperspectral image (HSI) change detection (CD) plays a crucial role in remote sensing observation. It leverages the abundant spectral and spatial information in bi-temporal HSIs to identify subtle Earth surface changes. Most current deep-learning-based HSI CD methods primarily utilize convolutional neural networks or transformers to extract features from bi-temporal images. However, these methods lack an effective attention mechanism to enhance differential features. In addition, they do not fully leverage the aggregation relationship between the features of bi-temporal images to extract interaction features. To address these challenges, we propose a novel adaptive information weighting and synchronized enhancement network (AIWSEN) for HSI CD. This network employs the information entropy to capture change features specific to the CD task and enhances bi-temporal interaction features. Specifically, an adaptive information weighting attention module (AIWAM) leverages the maximum discrete entropy theorem to capture the difference information. A dual-time synchronic change enhancing module (DSCEM) is designed to extract features by interactively aggregating features from bi-temporal HSIs to enhance difference features. A bi-temporal image feature selection and fusion module (BFSFM) is constructed to filter out important features using forget and update gates. Experimental results on three HSI CD datasets demonstrate that the proposed AIWSEN method outperforms several state-of-the-art methods. The source code of the proposed AIWSEN will be released at https://github.com/creativeXin/AIWSEN.