Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Images

It is a challenging task to integrate the spatial, temporal, and spectral information of multi-source remote sensing images, especially in the case of heterogeneous images. To this end, for the first time, this paper proposes a heterogeneous integrated framework based on a novel deep residual cycle generative adversarial network (GAN). The proposed network consists of a forward fusion part and a backward degeneration feedback part. The forward part generates the desired fusion result from the various observations; the backward degeneration feedback part considers the imaging degradation process and regenerates the observations inversely from the fusion result. The heterogeneous integrated fusion framework supported by the proposed network can simultaneously merge the complementary spatial, temporal, and spectral information of multi-source heterogeneous observations to achieve heterogeneous spatio-spectral fusion, spatio-temporal fusion, and heterogeneous spatio-temporal-spectral fusion. Furthermore, the proposed heterogeneous integrated fusion framework can be leveraged to relieve the two bottlenecks of land-cover change and thick cloud cover. Thus, the inapparent and unobserved variation trends of surface features, which are caused by the low-resolution imaging and cloud contamination, can be detected and reconstructed well. Images from many different remote sensing satellites, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, Sentinel-1, and Sentinel-2, were utilized in the experiments conducted in this study, and both the qualitative and quantitative evaluations confirmed the effectiveness of the proposed image fusion method.