Browsing by Author "Cs, A."

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    CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
    (Elsevier B.V., 2020) Kanu, S.; Khoja, R.; Lal, S.; Raghavendra, B.S.; Cs, A.
    Cloud Detection is an important pre-processing step for any application involving remote sensing data. This paper presents a deep learning based CloudX-Net architecture, that can detect cloud cover with improved accuracy in comparison to the benchmark from satellite remote sensing images. The proposed CloudX-Net model reduces the number of parameters needed for accurate predictions and thus make deep learning based cloud detection method very efficient. Atrous Spatial Pyramid Pooling (ASPP) and Separable convolution are used to optimize the network. For experimentation, we have used Landsat 8 images and 38-Cloud dataset and trained the architectures using Soft Jaccard loss function. Comparing several quantifying metrics result from various recent deep learning architectures proves the efficiency and effectiveness of the proposed CloudX-Net model for cloud detection from satellite images. The source code and data are available at https://github.com/shyamfec/CloudXNet. © 2020 Elsevier B.V.
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    Dehazing of Satellite Images using Adaptive Black Widow Optimization-based framework
    (Taylor and Francis Ltd., 2021) Suresh, S.; Rajan, M.; Pushparaj, J.; Cs, A.; Lal, S.; Chintala, C.S.
    Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene. © 2021 Informa UK Limited, trading as Taylor & Francis Group.