基于CRISM高光谱数据的火星表面矿物分类综述

IF 1.4 4区 地球科学 Q4 ENVIRONMENTAL SCIENCES
Priyanka Kumari, Sampriti Soor, Amba Shetty, Shashidhar G. Koolagudi
{"title":"基于CRISM高光谱数据的火星表面矿物分类综述","authors":"Priyanka Kumari, Sampriti Soor, Amba Shetty, Shashidhar G. Koolagudi","doi":"10.1117/1.jrs.17.041501","DOIUrl":null,"url":null,"abstract":"The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.","PeriodicalId":54879,"journal":{"name":"Journal of Applied Remote Sensing","volume":"7 4","pages":"0"},"PeriodicalIF":1.4000,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mineral classification on Martian surface using CRISM hyperspectral data: a survey\",\"authors\":\"Priyanka Kumari, Sampriti Soor, Amba Shetty, Shashidhar G. Koolagudi\",\"doi\":\"10.1117/1.jrs.17.041501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.\",\"PeriodicalId\":54879,\"journal\":{\"name\":\"Journal of Applied Remote Sensing\",\"volume\":\"7 4\",\"pages\":\"0\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/1.jrs.17.041501\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/1.jrs.17.041501","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

紧凑的火星侦察成像光谱仪(CRISM)大大提高了我们对火星矿物学的了解。凭借其增强的光谱和空间分辨率,CRISM能够识别和表征火星表面的各种矿物,为了解火星过去的气候和地质历史,以及地球大气和气候的演变提供有价值的见解。我们提出了一个全面的审查矿物鉴定在火星上使用CRISM数据。我们讨论了数据描述、预处理技术、不同的谱库、用于矿物识别的地质特征、挑战以及用于矿物分类的方法,如学习模型、概率方法和神经网络。我们重点介绍了火星表面矿物的主要发现,并讨论了验证技术。最后,我们讨论了进一步的研究,以解决该领域现有的差距和挑战。总的来说,我们利用CRISM数据提供了对矿物分类的一般理解,可以为对行星遥感和火星表面矿物识别感兴趣的研究人员和科学家提供有用的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mineral classification on Martian surface using CRISM hyperspectral data: a survey
The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Applied Remote Sensing
Journal of Applied Remote Sensing 环境科学-成像科学与照相技术
CiteScore
3.40
自引率
11.80%
发文量
194
审稿时长
3 months
期刊介绍: The Journal of Applied Remote Sensing is a peer-reviewed journal that optimizes the communication of concepts, information, and progress among the remote sensing community.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信