深空探测学报（中英文）2025，Vol.12Issue(6)：639-651,13.DOI:10.3724/j.issn.2096-9287.2025.20250044
基于深度学习的月球南极连续光照区智能提取方法
Intelligent Identification of Continuously Illuminated Regions at Lunar South Pole Based on Deep Learning
摘要
Abstract
Taking the connecting ridge between Shackleton and de Gerlache craters as the research area,based on real-time illumination simulation data from November 1,2026,to February 28,2027,a dynamic illumination dataset with a spatial resolution of 20 m/pixel and a temporal resolution of 1 hour was constructed.A deep-learning framework is proposed to recognize regions with continuous 3-day illumination,in which an improved VGG network extracts illumination-friendly regions from each temporal frame,a bidirectional GRU network captures temporal illumination characteristics,and a consistent temporal-spatial attention mechanism highlights key spatiotemporal illumination features.An output head network integrates these features to generate target regions.Based on the extracted regions and an eight-direction rover mobility model,a Sun-synchronous A* path planning algorithm is further optimized to enable illumination-aware navigation.Simulation results demonstrate that the proposed method accurately recognizes 3-day consecutive illumination-friendly regions in the 20 m/pixel dynamic dataset and effectively supports efficient rover path planning in well-illuminated areas of the lunar south pole.关键词
月球极区/动态光照/深度学习/时空注意力机制/路径规划Key words
Lunar polar region/illumination for dynamic scenes/deep learning/spatial-temporal attention mechanism/path planning分类
航空航天引用本文复制引用
陈杨,魏广飞,张浩,陆剑峰,苗清亮..基于深度学习的月球南极连续光照区智能提取方法[J].深空探测学报（中英文）,2025,12(6):639-651,13.基金项目
黔科合基础-ZK[2023]一般476 （）
国家重点研发计划资助(2022YFF0711400) （2022YFF0711400）
国家自然科学基金(42473053) （42473053）
安徽省自然科学基金(2408085Y021) （2408085Y021）
