In recent years,with the increase in the number of on-orbit remote sensing satellites and advancements in hyperspec-tral imaging technology,there has been a sharp rise in the volume of available hyperspectral data,marking an era of big data applications and data-driven scientific discoveries.However,this substantial and wide-ranging volume of hyperspectral data po-ses significant challenges for deep learning algorithms to learn and infer on a single node,hindering real-time and efficient intel-ligent interpretation of information.Therefore,there is a need for comprehensive multi-satellite resource distributed coopera-tive analysis to address the block effects caused by block processing.However,collaborative processing inherently involves in-formation interaction and transmission,necessitating gradient compression to reduce the transmitted information further,thereby alleviating communication bottlenecks in distributed learning.This paper comprehensively discusses various main-stream efficient communication gradient compression algorithms,specifically focusing on their pros and cons in communication-constrained on-orbit environments,and provides insights into the developmental trends of gradient compression.Through ex-tensive experimental comparisons,we comprehensively evaluate the performance of various gradient compression methods in hyperspectral image processing.These experiments demonstrate the applicability and performance differences of different methods,providing robust references for selecting the most suitable gradient compression methods in practical applications in the future.
万方数据
维普
