MSFA-Net : Multiple Spatial-Channel Feature Aggregation Network for Change Detection and a UAV-CD Dataset

Change detection in remote sensing images is pivotal for monitoring and comprehending dynamic environmental phenomena. Nonetheless, conventional change detection models have grappled with correlating information between channel and spatial dimensions due to inherent feature extraction limitations. Hence, this paper proposes an innovative change detection framework and a high resolution UAV change detection dataset named UAV-CD dataset. The introduced network embraces a Siamese network, amalgamating a feature extraction backbone network along with spatial and channel reconstruction convolution (ScConv) and Ghost modules. A primary contribution of this paper is the incorporation of ScConv into the change detection network, facilitating the reconstruction of information in both spatial and channel dimensions. Additionally, the Ghost module is employed to fortify information across distinct channel dimensions within the feature maps. Compared with current state-of-the-art methods, it is indicated that the proposed approach achieves superior performance on the LEVIR-CD, SYSU-CD, and our proposed dataset UAV-CD.