Faculty Publications
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Publications by NITK Faculty
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Item Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data(Institute of Electrical and Electronics Engineers Inc., 2024) Kumari, P.; Soor, S.; Shetty, A.; Daya Sagar, B.S.Creating a supervised learning model for mineral identification is challenging due to the lack of ground-truth data. This study utilizes a method from existing literature that generates a training dataset by augmenting available spectra in the MICA spectral library. However, rather than using entire spectra for identification, this study extracts spectral features from each spectrum for model training. It employs the apparent continuum removal method, Segmented Curve Fitting, to identify the most informative or distinguishable parts in the spectral domain. Spectral features are then extracted based on band-centers and band-areas for each selected part. The model is evaluated against a Targeted Reduced Data Record (TRDR) dataset obtained using a hierarchical Bayesian model, demonstrating improved identification performance than the existing supervised models. Finally, using this model, dominant minerals are identified in MTRDR data from the Nilli Fossae region of Mars, and a corresponding mineral map is presented. © 2024 IEEE.Item Mineral classification on Martian surface using CRISM hyperspectral data: a survey(SPIE, 2023) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface. © 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)