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Communication Dans Un Congrès Année : 2023

MangNet: A Muti-Scale Contextual Information Driven Deep Learning Architecture designed to perform Sunderban Landcover classification

Résumé

Automated classification of land-covers from satellite images using machine learning and deep learning approaches have garnered significant attention among researchers in the computer vision field in recent years due to the real-life significance of this application. Among various types of land covers mangroves stand out as a unique ecosystem that plays a crucial role in regulating the climate in coastal areas and providing a habitat for diverse flora and fauna. These characteristics make mangrove vegetation particularly significant. In this work, we present a novel and computationally efficient deep learning architecture called MangNet, designed to effectively classify diverse land covers in the Sunderban region of West Bengal, India. MangNet exploits its multi-scale contextual feature extraction capability to simultaneously classify multiple pixels within a patch, considering the interrelationship between neighboring pixels, which enhances its performance efficiency. The designed architecture achieved an impressive classification accuracy of 99.64%, surpassing existing state-of-the-art methods. We have used freely accessible dual-polarized Sentinel-1 data from the chosen Region of Interest (RoI) to conduct this study and demonstrated the relevance of intensity and phase values in each polarized channel for classification in the Results section.
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Dates et versions

hal-04355024 , version 1 (20-12-2023)

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Citer

S.K. Daud Hassan, Sripama Banerjee, Debanka Pal, Sheli Sinha Chaudhuri, Khalifa Djemal, et al.. MangNet: A Muti-Scale Contextual Information Driven Deep Learning Architecture designed to perform Sunderban Landcover classification. Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA 2023), Oct 2023, Paris, France. pp.1-6, ⟨10.1109/IPTA59101.2023.10320081⟩. ⟨hal-04355024⟩
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