Novel Spatial-Spectral Channel Attention Neural Network for Land Cover Change Detection with Remote Sensed Images

Land cover change detection (LCCD) with remote-sensed images plays an important role in observing Earth's surface changes. In recent years, the use of a spatial-spectral channel attention mechanism in information processing has gained interest. In this study, aiming to improve the performance of LCCD with remote-sensed images, a novel spatial-spectral channel attention neural network (SSCAN) is proposed. In the proposed SSCAN, the spatial channel attention module and convolution block attention module are employed to process pre- and post-event images, respectively. In contrast to the scheme of traditional methods, the motivation of the proposed operation lies in amplifying the change magnitude among the changed areas and minimizing the change magnitude among the unchanged areas. Moreover, a simple but effective batch-size dynamic adjustment strategy is promoted to train the proposed SSCAN, thus guaranteeing convergence to the global optima of the objective function. Results from comparative experiments of seven cognate and state-of-the-art methods effectively demonstrate the superiority of the proposed network in accelerating the network convergence speed, reinforcing the learning efficiency, and improving the performance of LCCD. For example, the proposed SSCAN can achieve an improvement of approximately 0.17-23.84% in OA on Dataset-A.

Automated summary

A novel spatial-spectral channel attention neural network (SSCAN) is proposed to improve the performance of land cover change detection (LCCD) with remote-sensed images. The SSCAN employs spatial channel attention module and convolution block attention module to process pre- and post-event images, amplifying the change magnitude among the changed areas and minimizing the change magnitude among the unchanged areas. Comparative experiments show that the proposed network outperforms other methods in terms of accelerating network convergence speed, reinforcing learning efficiency, and improving LCCD performance.