LAUSR

With the development of Earth observation technology, more multisource remote sensing images are obtained from various satellite sensors and significantly enrich the data source of change detection (CD). However, the utilization of multisource bitemporal images frequently introduces challenges during featuring or representing the various physical mechanisms of the observed landscapes and makes it more difficult to develop a general model for homogeneous and heterogeneous CD adaptively. In this article, we propose an adaptive spatial-spectral transformer CD network based on spectral token guidance, named STCD-Former. Specifically, a spectral transformer with dual-branch first encodes the diverse spectral sequence in spectral-wise to generate a corresponding spectral token. And then, the spectral token is used as guidance to interact with the patch token to learn the change rules. More significantly, to optimize the learning of difference information, we design a difference amplification module to highlight discriminative features by adaptively integrating the difference information into the feature embedding. Finally, the binary CD result is obtained by multilayer perceptron. The experimental results on three homogeneous datasets and one heterogeneous dataset have demonstrated that the proposed STCD-Former outperforms the other state-of-the-art methods qualitatively and visually.