Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory
Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory
复制标题
DOI：
10.1016/j.icarus.2012.11.029
复制
发表时间：
2013-03-01
期刊：
影响因子：
3.2
通讯作者：
Bell, James F., III
中科院分区：
文献类型：
--
作者：
Anderson, Ryan B.;Bell, James F., III
为了基于多光谱成像数据推断有关遥远目标的成分信息，我们研究了将火星探测车 (MER) Pancam 多光谱遥感观测与古谢夫陨石坑和子午线平原 MER 现场现场 α 粒子 X 射线光谱仪 (APXS) 衍生的元素丰度和穆斯堡尔 (MB) 衍生的含铁相丰度相关联的方法。这些数据集之间的大多数偏相关系数不具有统计显着性。将目标限制为那些被岩石磨损工具 (RAT) 磨损的目标可以改善 Pearson 相关性，最显着的是红蓝比 (673 nm/434 nm) 和含 Fe3+ 相之间的相关性，但部分相关性并不具有统计显着性。将 Pancam II 彩色可见光到近红外（VNIR；类似于 400-1000 nm）“光谱”与 APXS 和穆斯堡尔元素或矿物丰度相关的偏最小二乘 (PLS) 计算显示总体性能较差，尽管成分异常值的存在导致 Meridiani 数据的 PLS 结果得到改善。当通过预测 Gusev 目标的辉石含量来测试辉石的 Meridiani PIS 模型时，结果很差，这表明 Meridiani 的 PLS 模型不适用于其他站点的数据。古谢夫陨石坑数据的类类比软独立建模 (SIMCA) 分类显示出好坏参半的结果。在具有已知类别的 24 个 Gusev 测试感兴趣区域 (ROI) 中，11 个 ROI 中的像素分类正确，而其他区域分类错误或未分类，APXS 和 Mossbauer 数据的 k 均值聚类用于将 Meridiani 目标分配给组合类别。聚类派生的类别对应于有意义的地质和/或颜色单位差异，并且使用这些类别的 SIMCA 分类在某种程度上是成功的，在已知类别的 11 个 RO 中，有超过 30% 的像素被正确分类到 9 个 RO 中。这项工作表明，SWIR 多光谱成像数据与 APXS 和 Mossbauer 衍生的成分/矿物学之间的关系通常很弱，考虑到 SWIR 成像（最高几微米）与其他类型的表面采样深度不同，这也许并非完全出人意料的结果。 APXS（数十μm）和MB测量（数百μm）。然而，即将推出的火星科学实验室 (MSL) 漫游车的 ChemCam 激光诱导击穿光谱 (LIBS) 仪器的结果可能显示出与 Mastcam SWIR 多光谱观测更密切的关系，因为对目标的初始激光发射只会分析表面的上部几微米。本研究中使用的聚类和分类方法可以应用于任何数据集，以形式化类别的定义并识别不适合先前定义的类别的目标。由爱思唯尔公司出版
In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mossbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson's correlations, most notably between the red-blue ratio (673 nm/ 434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam II-color visible to near-IR (VNIR; similar to 400-1000 nm) "spectra" to APXS and Mossbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PIS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified, k-Means clustering of APXS and Mossbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROls with known classes.This work shows that the relationship between SWIR multispectral imaging data and APXS- and Mossbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of mu m) and MB measurements (hundreds of mu m). Results from the upcoming Mars Science Laboratory (MSL) rover's ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes. Published by Elsevier Inc.