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• W2067994180 abstract "Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base f..." @default.
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• W2067994180 date "2011-09-08" @default.
• W2067994180 modified "2023-09-24" @default.
• W2067994180 title "An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images" @default.
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• W2067994180 doi "https://doi.org/10.1080/01431161.2011.604652" @default.
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