摘要
火星车(即巡视探测器)是对火星表面探测和科学研究的重要手段。针对火星车采集到的日益增长的遥感数据,亟需一种能够智能化地从海量影像中探测出有科学价值的新颖目标的方法。传统的新颖探测多采用基于距离测度和基于影像重建的方法,其中基于距离测度的方法逐像素计算新颖分数,未考虑空间上下文信息;基于影像重建的方法侧重对典型地貌背景进行重建,新颖性表现为影像重建误差,对小型新颖目标如钻孔、除尘点等提取效果不佳。提出一种改进的火星车多光谱影像深度新颖目标探测方法(convolution auto-encoder combined Mahalanobis distance method,CAE-M),利用全卷积自编码神经网络提取深层特征进行典型地貌重建,并联合马氏距离将新颖目标与典型地貌背景分离,充分挖掘空间维与光谱维特征,提高火星车新颖目标探测结果的准确性。实验采用好奇号火星车多光谱影像数据集,在盖尔撞击坑地表采用Reed-Xiaoli探测器、主成分分析、卷积自编码神经网络、生成对抗网络与CAE-M进行对比实验,结果表明,CAE-M在探测精度和可视化解释上均优于对比方法,在不同类别的新颖目标探测上都有着均衡稳定的表现。
Objectives: Mars is the main target object for deep space exploration. Mars rovers, or roving probes, are important tools for surface exploration and scientific research on Mars. For the growing amount of remote sensing data collected by Mars rovers, there is an urgent need for a method that can intelligently detect novel targets of scientific value from the massive amount of images, reduce the time cost of detection planning, and provide information for subsequent scientific analysis. The traditional novel detection methods mostly include distance-based metrics and image-based reconstruction methods, distance-based metrics calculate novel scores pixel by pixel without considering spatial contextual information, and image-based reconstruction methods focus on reconstruction of typical landscape backgrounds, and novelty is manifested by image reconstruction errors, which is not effective in extracting small novel targets such as boreholes and dust removal points.Methods: To address the above problems of traditional novel detection methods in Mars rover novel target detection, this paper proposes an improved Mars rover multispectral image depth novel target detection method, uses full convolutional self-coding neural network to extract deep features for typical landscape reconstruction, and joints Mahalanobis distance for novel target and typical landscape background separation, fully exploits the spatial and spectral dimensional features to improve the accuracy of Mars rover novel target detection results.Results: The experiments use the multispectral image dataset of Curiosity rover released by NASA( National Aeronautics and Space Administration), and the proposed convolution auto-encoder combined Mahalanobis distance method(CAE-M) is compared with Reed-Xiaoli detector, principal component analysis, convolution auto-encoder convolution, and generative adversarial networks on the surface of Gale crater. The results show that CAE-M outperforms previous detection methods in terms of detection accuracy and visual int
作者
赵之若
王少宇
王心宇
钟燕飞
ZHAO Zhiruo;WANG Shaoyu;WANG Xinyu;ZHONG Yanfei(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China)
出处
《武汉大学学报(信息科学版)》
EI
CAS
CSCD
北大核心
2022年第8期1328-1335,1348,共9页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(42071350,42101327)
中央高校基本科研业务费(2042021kf0070)。
关键词
深空探测
火星车
多光谱影像
新颖目标探测
深度学习
deep space exploration
rover
multispectral image
novel target detection
deep learning