Faculty Publications
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Item Remote sensing image based nearshore bathymetry extraction of Mangaluru coast for planning coastal reservoir(Elsevier, 2020) Kumari, P.; Ramesh, H.Water is an essential source for numerous day-to-day necessities. The storage of flood water in the nearshore region of sea by constructing a dyke is called coastal reservoir. Bathymetry of nearshore region is necessary, which is extracted through remote sensing technique in Mangaluru coast, India, to plan and identify a suitable site for coastal reservoir in this study. Single linear regression (SLR) and multi linear regression (MLR) algorisms with Landsat 8 OLI/TIRS data were used and calibrated with hydrographic charts. The SLR method produced bathymetry with coefficient of determination of 0.76 and 0.70 for Blue and NIR bands, respectively, and 0.88 for combination of Blue and NIR bands in MLR method, which is considerably high and accurate. The depth obtained was in the range of 0-20m, from the shoreline up to 10km offshore. The study helps in determining area and volume of the coastal reservoir. © 2020 Elsevier Inc.Item Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images(Institute of Electrical and Electronics Engineers Inc., 2024) Soor, S.; Kumari, P.; Daya Sagar, B.S.; Shetty, A.Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces. © 2024 IEEE.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 Segmented Curve-Fitting Method for Continuum Removal in CRISM MTRDR data(Copernicus Publications, 2025) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.A spectrum in a multiband remotely sensed image is generally a mixture of spectra of different materials present in the scene which can be distinguished by distinct absorption signatures. A mixed spectrum possesses a smooth baseline shape, known as a continuum, that masks the individual spectral features. Continuum can also appear due to instrument artifacts and topographic illumination effects. Eliminating the continuum from a spectrum being analyzed and correctly identifying its unique absorption characteristics are crucial for material identification, traditionally achieved by the apparent continuum removal methods like using an Upper Convex Hull (UCH). Nevertheless, most of these methods struggle when baseline curvature exceeds certain limits, often combining distinct absorptions. In this paper, a new apparent continuum removal technique called Segmented Curve-Fitting (SCF) is proposed, which requires no prior information about the spectrum but excels in accurately extracting distinct absorptions, even in the presence of significant curvature. The performance of SCF is compared with UCH and a few other apparent continuum removal methods previously used in literature, using a collection of simulated data of varying complexity as well as a real CRISM TRDR hyperspectral dataset. The identification score is improved by around 8% for the similarity matching method Weighted Sum of Spectrum Correlation and by around 1.5% for a Convolutional Neural Network. Furthermore, an SCF-based mineral identification framework demonstrates its effectiveness in identifying the dominant minerals on CRISM MTRDR hyperspectral data collected from different locations on the Martian surface. © © 2025 Priyanka Kumari et al.Item A UNet Model for Accelerated Preprocessing of CRISM Hyperspectral Data for Mineral Identification on Mars(Copernicus Publications, 2025) Kumari, P.; Soor, S.; Shetty, A.; Nair, A.M.Accurate mineral identification on the Martian surface is critical for understanding the planet’s geological history. This paper presents a UNet-based autoencoder model for efficient spectral preprocessing of CRISM MTRDR hyperspectral data, addressing the limitations of traditional methods that are computationally intensive and time-consuming. The proposed model automates key preprocessing steps, such as smoothing and continuum removal, while preserving essential mineral absorption features. Trained on augmented spectra from the MICA spectral library, the model introduces realistic variability to simulate MTRDR data conditions. By integrating this framework, preprocessing time for an 800 × 800 MTRDR scene is reduced from 1.5 hours to just 5 minutes on an NVIDIA T1600 GPU. The preprocessed spectra are subsequently classified using MICAnet, a deep learning model for Martian mineral identification. Evaluation on labeled CRISM TRDR data demonstrates that the proposed approach achieves competitive accuracy while significantly enhancing preprocessing efficiency. This work highlights the potential of the UNet-based preprocessing framework to improve the speed and reliability of mineral mapping on Mars. © © 2025 Priyanka Kumari et al.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)Item A Fully-Automated Framework for Mineral Identification on Martian Surface Using Supervised Learning Models(Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.The availability of various spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data on NASA PDS (Planetary Data System) hugely facilitated the research on the surface mineralogy of Mars, however, building supervised learning models for mineral mapping appears to be challenging due to the lack of ground-truth/training data. In this paper, an automated framework is presented that classifies the spectra in a CRISM hyperspectral image using supervised learning models, where the required training data is produced by augmenting the mineral spectra available in the MICA (Minerals Identified in CRISM Analysis) spectral library, that keeps the key absorption signatures in the mineral spectra intact while providing adequate variability. The framework contains a pre-processing pipeline that in addition to some conventional pre-processing steps includes a new feature extraction method to capture the information of the most distinguishable absorption patterns in the spectra. The proposed framework is validated on a set of CRISM images captured from different locations on the Martian surface by using different types of supervised learning models, like random forests, support vector machines, and neural networks. An uncertainty analysis of the different steps involved in the pre-processing pipeline is provided, as well as a comparison of performances with some of the previously used methods for this purpose, which shows this framework works comparably well with a mean accuracy of around 0.8. Interactive mineral maps are also provided for the detected dominant minerals. © 2013 IEEE.Item MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface(Elsevier B.V., 2024) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective. © 2024