Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Lal, S.Subject: search.filters.subject.Remote sensing

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    A Framework for Quality Enhancement of Multispectral Remote Sensing Images
    (Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Das, D.; Lal, S.
    Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images. © 2017 IEEE.
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    Modified Dual Domain Network for SAR Change Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kevala, V.D.; Ravi, S.; Surya Kaushik, B.N.; Lal, S.
    Synthetic Aperture Radar (SAR) images are utilised for change detection analysis due to their all-weather imaging capabilities. This paper proposes modified dual domain network (MDDNet) for SAR change detection. We introduced the atrous spatial pyramid pooling block to extract multiscale characteristics in the spatial domain. The MDDNet extracts features from both the spatial and frequency domains. The proposed network is trained unsupervised with pre-classification output. The performances of proposed and existing SAR change detection models are evaluated on four bitemporal SAR datasets. The experimental results indicate that the results of proposed MDDNet is better than existing change detection models on four bitemporal SAR dataset. © 2024 IEEE.