CTSFFNet: Cross-Temporal Symmetric Feature Fusion Network for Hyperspectral Image Change Detection

Hyperspectral change detection (HCD) aims to identify the changed and unchanged pixels in bitemporal images, which has been applied in various aspects. Currently, many deep learning-based change detection methods have been developed. However, existing change detection methods only focus on changed or temporal information while neglecting the complementary information between them. To solve this issue, a novel cross-temporal symmetric feature fusion network (CTSFFNet) is proposed for change detection of hyperspectral images. First, we perform pixel-wise subtraction and concatenation on the multi-temporal hyperspectral images to obtain the difference data and temporal data, respectively. Then, a three-layer convolutional neural network is performed on the difference data and temporal data to yield the difference and temporal features. Finally, a cross-temporal symmetric feature fusion (CTSSF) module is designed to merge the extracted features followed by a fully connected layer to obtain the final change regions. Experiments on two popular datasets demonstrate that the proposed CTSSFNet achieves superior detection performance compared to other state-of-the-art methods.