It is a challenging task to integrate the spatial, temporal, and spectral information of multisource remote sensing images, especially in the case of heterogeneous images. To this end, for the first time, this article 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 multisource heterogeneous observations to achieve heterogeneous spatiospectral fusion, spatiotemporal fusion, and heterogeneous spatiotemporal–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.

中文翻译：

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基于深度学习的异构遥感影像时空谱融合融合

整合多源遥感图像的空间、时间和光谱信息是一项具有挑战性的任务，尤其是在异构图像的情况下。为此，本文首次提出了一种基于新颖的深度残差循环生成对抗网络（GAN）的异构集成框架。所提出的网络由前向融合部分和后向退化反馈部分组成。前向部分根据各种观察结果生成所需的融合结果；后向退化反馈部分考虑了成像退化过程，并从融合结果中逆向生成观察结果。所提出的网络支持的异构集成融合框架可以同时融合互补的空间、时间、和多源异构观测的光谱信息，实现异构时空融合、时空融合和异构时空-光谱融合。此外，所提出的异构集成融合框架可以用来缓解土地覆盖变化和厚云覆盖这两个瓶颈。因此，可以很好地检测和重建由低分辨率成像和云污染引起的地表特征不明显和未观察到的变化趋势。在本研究中进行的实验中使用了来自许多不同遥感卫星的图像，即中分辨率成像光谱仪 (MODIS)、Landsat 8、Sentinel-1 和 Sentinel-2，