LAUSR

As the number of aero and space remote sensing platforms increases, distributed observation and real-time terminal processing become mainstream in the future. However, most of the training methods for the multiplatform are still limited to centralized structures or independent training based on a single platform, which is inefficient or limited in accuracy. To solve this problem, we innovatively propose a distributed collaborative method (DCM) for remote sensing image classification training in this article. First, the proposed training method, which is based on one cloud and several terminals, can aggregate different parameters of the terminal network to the cloud to improve global accuracy. Second, a sample proximity network (SPN) is designed to process the problem of data heterogeneity on different terminal networks, which further improves the accuracy during the model fusion on the cloud. Third, a multilayer grouped concatenation module is applied after the model fusion to extract hierarchical features with different categories of remote sensing images. Experimental results on the challenging remote sensing image classification dataset FAIR1M show that the proposed training method has better collaborative learning ability than the centralized-based model or terminal-trained lightweight network under the heterogeneous data.