Adversarial learning network for multi-source change detection in built-up areas: integrating VHR RGB and multispectral imagery

The rapid advancement of remote sensing technologies and the increasing availability of satellite and aerial imagery have created new opportunities for change detection (CD). The existing deep learning (DL) methods often focus on single-source images, such as very high-resolution (VHR) RGB images, but struggle due to the limited spectral information and imaging inconsistencies within them. To overcome these limitations, this paper proposes an innovative end-to-end adversarial network with dual-encoders that extracts spectral, spatial, and textural features from multiple image sources for CD in built-up areas. The generator network of the proposed network employs a dual-encoding architecture that simultaneously processes VHR RGB and multispectral bi-temporal images and a decoder architecture that incorporates a channel attention module to improve the construction of the change map (CM). The discriminator network of the proposed network evaluates the authenticity of the generated CM, refining feature differentiation between changed and unchanged regions. Conducting experiments on two public datasets demonstrates that our method significantly outperforms existing DL approaches, highlighting the effectiveness of adversarial training in improving multi-source CD reliability in urban environments.