DotWang/HKNAS
[TNNLS 2023] The official repo for the paper "HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search".
This tool helps remote sensing specialists, environmental scientists, and urban planners automatically identify and classify different materials or land cover types from hyperspectral satellite or aerial imagery. It takes raw hyperspectral image data as input and produces highly accurate classifications (e.g., distinguishing crop types, minerals, or urban structures). This is for professionals who need to precisely categorize ground features.
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Use this if you are working with hyperspectral imagery and need to perform highly accurate pixel-level or spatial classification and segmentation tasks on the data.
Not ideal if you are not working with hyperspectral image data or do not have a strong understanding of machine learning model training.
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Oct 13, 2024
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