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Validation of spectral unmixing algorithms applied on CRISM/MRO hyperspectral images

机译:验证Crism / MRO高光谱图像上应用的光谱解密算法

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As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this article, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High Resolution Imaging Science Experiment (HiRISE) instruments. In late winter, this area of Mars is appropriate for testing linear unmixing techniques because of the geographical coexistence of seasonal CO2 ice and defrosting dusty features, which is not resolved by CRISM. Linear unmixing is carried out on a selected CRISM image by a seven state-of-the-art approaches based on different principles. Processing of HiRISE imagery allows the construction of a ground truth in the form of a reference abundance map related to the defrosting features. Validation of abundances estimated by spectral unmixing is carried out in an independent and quantitative manner by comparison to the ground truth.
机译:随着行星勘探的高光谱数据的增加,有效但精确的算法是对其分析的决定性。在本文中,研究了用于分析来自火星的高光谱图像的光谱解密的能力。为此目的,我们考虑由MARS(CRISM)的紧凑型侦察成像光谱仪观察到的罗素梅根群,以及高分辨率成像科学实验(HIRISE)仪器。在冬季,这一领域是由于季节性CO 2 冰和除霜尘土尘埃的地理共存,适用于测试线性解密技术,该技术不会被Crism解决。基于不同原理的七种最先进的方法,在所选择的CRISM图像上进行线性解密。 Hirise图像的处理允许以与除霜功能相关的参考丰富图的形式构建地面真理。通过与地面真理相比,以独立和定量的方式验证由光谱解密估计的丰富。

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