轻量化类火星地貌快速识别分类方法研究  

Lightweight Rapid Recognition and Classification for Mars Analog Terrain

作　　者：李建龙 张立华 何梨梨 刘欣鑫 杨彬 周继时 LI Jianlong;ZHANG Lihua;HE Lili;LIU Xinxin;YANG Bin;ZHOU Jishi(School of Electrical and Information Engineering,HuNan University,Changsha 410082,China;Lunar Exploration and Space Engineering Center,China National Space Administration,Beijing 100190,China;Deep Space Exploration Lab,Beijing100190,China)

机构地区：[1]湖南大学电气与信息工程学院,长沙410082 [2]国家航天局探月与航天工程中心,北京100190 [3]深空探测实验室,北京100190

出　　处：《深空探测学报(中英文)》2025年第1期77-85,共9页Journal Of Deep Space Exploration

摘　　要：类火星地貌识别分类旨在通过分析类火星地貌图像,模拟研究火星环境,对火星形成演化及潜在宜居环境探索等科学问题具有重要研究意义。针对目前火星地貌分类算法难以平衡模型分类效果和轻量化程度的问题,提出一种轻量化类火星地貌快速识别分类方法(Lightweight Rapid Recognition and Classification for Mars Analog Terrain,LWNet),构建双分支教师-学生网络,利用知识蒸馏技术减少模型参数量与计算量,并嵌入注意力机制提高对地貌类型的分类识别能力,实现分类模型的高精度和轻量化。为验证所提方法的分类性能表现,通过采集地球上相似地貌,构建了包括悬崖断面、沙漠、河道及雅丹在内的4种典型类火星地貌数据集,每种地貌类型各获取800张图像,用于LWNet开展快速识别分类实验。结果表明,LWNet总体分类精度达到97.81%,相较于精度最高的Swin-Transformer仅下降了1.25%,而其参数量和计算量却只有Swin-Transformer的1.3%和4.8%,验证了所提轻量化类火星地貌快速识别分类方法的有效性和优越性。Recognition and classification of Mars analog terrain aim to simulate and study the Mars environment by analyzing Mars analog terrain images,which holds significant research value for exploring scientific questions such as formation,evolution,and potential habitability of Mars.In response to the challenge of balancing classification performance and model lightweighting in current Mars terrain classification algorithms,a lightweight,rapid recognition and classification method for Mars analog terrain is proposed(LWNet).This algorithm constructs a dual-branch teacher-student network,employs knowledge distillation to reduce the number of parameters and computational load of the model,and integrates attention mechanism to enhance the capability of terrain classification and recognition,achieving high accuracy and lightweight classification models.To verify the classification performance of the proposed method,a dataset of Mars analog terrain on Earth was established,including four typical Mars landforms:cliff,desert,channel,and yardang,with each type of terrain consisting of 800 images.The dataset was employed to conduct rapid recognition and classification experiments with LWNet.The results indicate the overall accuracy reaches 97.81%,which only decreases by 1.25%compared with Swin-Transformer,while its Parameters and FLOPs are only 1.3%and 4.8%of Swin-Transformer,respectively.Experimental results verify the effectiveness and superiority of the LWNet.

关 键 词：类火星地貌 轻量化 知识蒸馏 图像分类 

分 类 号：P185.3[天文地球—天文学]