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Original title:
Predicting spectral channels related to mineralogy from CRISM spectral bands using deep learning
Authors: SAHANI, Aman
Document type: Master’s theses
Year: 2024
Language: eng
Abstract: The Mars Reconnaissance Orbiter (MRO) is equipped with a suite of instruments that collectively generate a vast and diverse array of imaging data for comprehensive investigations of the Martian surface. Notably, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures reflective information across multiple spectral bands. However, CRISM coverage is partial, limiting the availability of full spectral and spatial resolution data and the instrument is no longer working. In contrast, the Colour and Stereo Surface Imaging System (CaSSIS), characterized by high spatial resolution and diverse color capabilities, covers larger areas of the Martian surface. The goal of this project is to improve the accuracy of predicting specific spectral signatures, such as carbonates, using spectral reflectance data. I want to strip CRISM images to CaSSIS bands and then use the generative abilities of diffusion model to predict the carbonate signature.
Keywords: Carbonates; Colour and Stereo Surface Imaging System (CaSSIS); Compact Reconnaissance Imaging Spectrometer for Mars (CRISM); Encoder; Mars Reconnaissance Orbiter; PCA; Stable Diffusion; Unet
Citation: SAHANI, Aman. Predicting spectral channels related to mineralogy from CRISM spectral bands using deep learning. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/76846
Permalink: http://www.nusl.cz/ntk/nusl-695377
The record appears in these collections:
Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Master’s theses
Authors: SAHANI, Aman
Document type: Master’s theses
Year: 2024
Language: eng
Abstract: The Mars Reconnaissance Orbiter (MRO) is equipped with a suite of instruments that collectively generate a vast and diverse array of imaging data for comprehensive investigations of the Martian surface. Notably, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures reflective information across multiple spectral bands. However, CRISM coverage is partial, limiting the availability of full spectral and spatial resolution data and the instrument is no longer working. In contrast, the Colour and Stereo Surface Imaging System (CaSSIS), characterized by high spatial resolution and diverse color capabilities, covers larger areas of the Martian surface. The goal of this project is to improve the accuracy of predicting specific spectral signatures, such as carbonates, using spectral reflectance data. I want to strip CRISM images to CaSSIS bands and then use the generative abilities of diffusion model to predict the carbonate signature.
Keywords: Carbonates; Colour and Stereo Surface Imaging System (CaSSIS); Compact Reconnaissance Imaging Spectrometer for Mars (CRISM); Encoder; Mars Reconnaissance Orbiter; PCA; Stable Diffusion; Unet
Citation: SAHANI, Aman. Predicting spectral channels related to mineralogy from CRISM spectral bands using deep learning. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/76846
Permalink: http://www.nusl.cz/ntk/nusl-695377
The record appears in these collections:
Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Master’s theses
Record created 2026-02-07, last modified 2026-02-07