Learning Starts From Optimizing the Composition of Temporal Information for Hyperspectral Change Detection

Hyperspectral image change detection (HSI-CD) is a task that utilizes both spectral and spatial features to more effectively detect changes. The features of HSI captured at different times are often influenced by external factors. The annoying variability is not useful for CD. Current methods extract effective information through complex learning together with it. They did not focus on whether different categories (changed and unchanged) of sample pairs have the same information composition. They lack a fundamental analysis and optimization based on the composition of information pairs to address current issues such as insufficient recognition of changed samples and poor feature fusion. To address these issues, we rethink the information composition of bitemporal sample pairs in different categories for HSI-CD, and design a frequency-domain information exchange and generation network with Siamese U-shaped structure (FDIEG-UNet) for HSI-CD. Our contribution can be summarized as follows: 1) by designing the FDGJLS module for learning and separating global-joint spectral-spatial features, to provide better basic frequency-domain information for subsequent feature-domain optimization; 2) based on the information from FDGJLS, we use the ULDT and SSTDFE modules to remove the influence of temporal-correlated invalid style information in feature fusion. Two modules work together to improve the equality of feature description between changed and unchanged samples in CD; and 3) the experimental results on three real HSI-CD datasets demonstrate the effectiveness of our proposed method in solving the above problems. The code is available at <uri>https://github.com/WUTCM-Lab/FDIEG-UNet</uri>.