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Hyperspectral data, acquired through sensors capable of capturing hundreds of narrow, contiguous spectral bands across the electromagnetic spectrum, has become a critical tool in modern remote sensing and GIS applications. Unlike multispectral imagery, which typically collects data in a few broad bands, hyperspectral data provides high spectral resolution, enabling the identification and analysis of subtle differences in material properties, vegetation types, mineral compositions, and man-made features. However, before hyperspectral data can be effectively used, it requires careful preprocessing to ensure accuracy and reliability. Preprocessing of hyperspectral data typically involves several steps, starting with radiometric correction. Radiometric correction addresses sensor-specific errors and variations in detector response, converting raw digital numbers into physically meaningful units such as reflectance. This step is essential for comparing spectral signatures across different images or over ______ ____ _____ ___ _______ _____ __________ _____ ____.
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