数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (4): 54-66.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.04.005

doi: 10.11871/jfdc.issn.2096-742X.2025.04.005

• 专刊:空间科学大数据智能算法模型与工具 • 上一篇    下一篇

火星矿物自动识别及分布制图软件研发

刘长卿*(),吕英波,凌宗成   

  1. 山东大学空间科学与技术学院,空间科学研究院山东 威海 264209
  • 收稿日期:2025-06-30 出版日期:2025-08-20 发布日期:2025-08-21
  • 通讯作者: 刘长卿
  • 作者简介:刘长卿,山东大学,副研究员,主要从事深空探测与行星光谱学研究。
    本文负责软件开发和内容撰写。
    LIU Changqing, associate researcher, Shandong University, is mainly engaged in research in the fields of deep space exploration and planetary spectroscopy.
    In this paper, he is primarily responsible for the software development and manuscript writing.
    E-mail: liucq@sdu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2022YFF0711400);空间科学大数据智能管理与分析挖掘关键技术及应用

Software Development for Martian Mineral Identification and Distribution Mapping

LIU Changqing*(),LYU Yingbo,LING Zongcheng   

  1. School of Space Sciences and Technology, Institute of Space Sciences, Shandong University, Weihai, Shandong 264209, China
  • Received:2025-06-30 Online:2025-08-20 Published:2025-08-21
  • Contact: LIU Changqing

摘要:

【目的】目前火星探测已获得海量成像光谱数据,但是对其光谱数据的处理和矿物识别耗时耗力,这对传统人工处理和目视解译方法提出较大挑战。本研究针对火星成像光谱数据的反演需求,开发了一套通用的火星成像光谱数据处理和矿物识别方法,并建立了一套自主的火星矿物自动识别及分布制图软件。【方法】本文开发的软件具备以下功能:(1) 矿物种类自动识别:该软件基于火星矿物的RELAB光谱数据,采用FATT方法自动识别火星表面的矿物种类;(2) 矿物空间分布制图:结合SAM、SID、SID_SA等多种光谱匹配方法,自动绘制矿物空间分布图;(3) 矿物光谱参数分布制图:基于矿物的可见近红外光谱特征(如吸收深度等),自动绘制矿物光谱参数分布图。【结果】本文采用预处理后的MMS、CRISM和OMEGA等成像光谱数据,验证了软件功能和性能。该软件可快速实现火星矿物种类识别和分布制图,极大节约了行星科学家对成像光谱数据的处理步骤和处理时间。

关键词: 火星, 可见近红外成像光谱, 遥感探测, 矿物分布, 软件开发

Abstract:

[Objective] Mars exploration missions have acquired massive imaging spectral data, presenting significant challenges to traditional manual processing and visual interpretation approaches. This study introduces a generalized methodology for processing Martian imaging spectral data, coupled with the development of a software for automatic Martian mineral identification and distribution mapping. [Methods] The software leverages the FATT algorithm based on RELAB spectral datasets to enable automated mineral type classification, integrates spectral matching techniques (SAM, SID, SID_SA) for generating spatial distribution maps, and derives spectral parameter distributions by analyzing visible-near-infrared features (e.g., absorption depth) of Martian minerals. [Results] The validation using preprocessed imaging spectral data from MMS, CRISM, and OMEGA payloads demonstrates that the software facilitates efficient mineral identification and distribution mapping, substantially streamlining processing workflows and reducing analysis time for planetary scientists.

Key words: Mars, visible and near-infrared imaging spectroscopy, remote sensing, mineral distribution, software development