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Examples of produced spectral cluster maps with a predefined number of 10 clusters from the FRT0001c71b dataset. (a) Autoencoder + GMM; (b) PCA + K-Means; (c) UMAP + Fuzzy-c-Means.
Source publication
Four-band color imaging of the Martian surface using the Color and Stereo Surface Imaging System (CaSSIS) onboard the European Space Agency’s ExoMars Trace Gas Orbiter exhibits a high color diversity in specific regions. Not only is the correlation of color diversity maps with local morphological properties desirable, but mineralogical interpretati...
Citations
... Publications using UMAP are more abundant in the biology research field [21,22]. In a previous work Fernandes et al. [23] made a detailed comparison of these techniques on datasets of Capri Chasmata within the VISNIR range. They reached promising results by applying UMAP as the dimensionality reduction technique and showed superior performance of this technique. ...
... The UMAP algorithm has achieved promising results by processing MTRDR CRISM datasets, as shown in Fernandes et al. [23]. They report superior performance of UMAP in comparison to other feature extraction techniques based on multiple scores. ...
... We extend the implementation by adding a new method for dimensionality reduction. As outlined in Section 2.2, we follow the approach of Fernandes et al. [23] and pick up the UMAP algorithm. ...
In this paper, we expand upon our previous research on unsupervised learning algorithms to map the spectral parameters of the Martian surface. Previously, we focused on the VIS-NIR range of hyperspectral data from the CRISM imaging spectrometer instrument onboard NASA’s Mars Reconnaissance Orbiter to relate to other correspondent imager data sources. In this study, we generate spectral cluster maps on a selected CRISM datacube in a NIR range of 1050–2550 nm. This range is suitable for identifying most dominate mineralogy formed in ancient wet environment such as phyllosilicates, pyroxene and smectites. In the machine learning community, the UMAP method for dimensionality reduction has recently gained attention because of its computing efficiency and speed. We apply this algorithm in combination with k-Means to data from Jezero Crater. Such studies of Jezero Crater are of priority to support the planning of the current NASA’s Perseversance rover mission. We compare our results with other methodologies based on a suitable metric and can identify an optimal cluster size of six for the selected datacube. Our proposed approach outperforms comparable methods in efficiency and speed. To show the geological relevance of the different clusters, the so-called “summary products” derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We show that clustered regions relate to different mineralogical compositions (e.g., carbonates and pyroxene). Finally the generated spectral cluster map shows a qualitatively strong resemblance with a given manually compositional expert map. As a conclusion, the presented method can be implemented for automated region-based analysis to extend our understanding of Martian geological history.
