Spatio–Temporal–Spectral Collaborative Learning for Spatio–Temporal Fusion with Land Cover Changes

Spatio-temporal fusion by combining the complementary spatial and temporal advantages of multi-source remote sensing images to obtain time-series high spatial resolution images is highly desirable in monitoring surface dynamics. Currently, deep learning (DL)-based fusion methods have received extensive attention. However, existing DL-based spatio-temporal fusion methods are generally limited in fusing the images with land cover changes. In this paper, we propose a spatio-temporal-spectral collaborative learning framework for spatio-temporal fusion to alleviate this problem. Specifically, the proposed method integrates the convolutional neural network and recurrent neural network into a unified framework, consisting of three sub-networks: multi-scale siamese convolutional neural network, multi-layer convolutional recurrent neural network, and adaptive weighting fusion network. The multi-scale siamese convolutional neural network has a flexible weight-sharing network to extract multi-scale spatial-spectral features from multi-source remote sensing images. The multi-layer convolutional recurrent neural network is constructed on the convolutional long-short term memory units to comprehensively learn the land cover changes by spatial, spectral, and temporal joint features. The adaptive weighting fusion network with a spatio-temporal-spectral change loss is proposed to further improve the interpretability and robustness. The experiments were performed on the publicly available benchmark datasets featured by phenology and land cover type changes, respectively. The experimental results demonstrated the competitive performance of the proposed method than other state-of-the-art fusion methods.