Faculty Publications
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Item Guided SAR image despeckling with probabilistic non local weights(Elsevier Ltd, 2017) Gokul, J.; Nair, M.S.; Rajan, J.SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. © 2017 Elsevier LtdItem 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