DegangWang97/IEEE_TGRS_PDBSNet
[TGRS 2023 ESI Highly Cited Paper (TOP 1%)] PDBSNet: Pixel-Shuffle Downsampling Blind-Spot Reconstruction Network for Hyperspectral Anomaly Detection
This tool helps geospatial analysts and remote sensing specialists identify unusual or unexpected objects within hyperspectral satellite imagery. You input a hyperspectral image, and it outputs an 'anomaly score' map, highlighting pixels that deviate significantly from their surroundings. This is particularly useful for tasks like environmental monitoring or defense applications where spotting irregularities is critical.
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Use this if you need to reliably find small, rare, or unexpected features in your hyperspectral satellite or aerial images without prior knowledge of what those anomalies might look like.
Not ideal if you are working with standard RGB or multispectral images, or if you already have labeled data for the specific anomalies you want to detect.
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Python
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GPL-2.0
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Jul 18, 2025
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