DegangWang97/IEEE_TGRS_DirectNet
[TGRS 2024 ESI Highly Cited Paper (TOP 1%)] Sliding Dual-Window-Inspired Reconstruction Network for Hyperspectral Anomaly Detection
This tool helps geospatial analysts and remote sensing specialists pinpoint unusual objects or features in satellite and airborne hyperspectral images. You provide an unlabeled hyperspectral image, and it outputs a map highlighting areas that deviate significantly from their surroundings. This is ideal for identifying anomalies like unexpected geological formations, environmental changes, or specific target detection.
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Use this if you need to automatically detect rare or unusual pixels in hyperspectral satellite or airborne imagery without prior knowledge or labels of what constitutes an anomaly.
Not ideal if you are working with standard RGB images, multispectral data, or if you already have labeled data for your target objects.
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Python
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GPL-2.0
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Feb 28, 2024
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