yc-cui/Super-AD
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
This tool helps scientists and researchers in fields like remote sensing or geology to automatically identify unusual or anomalous regions in hyperspectral images. You provide raw hyperspectral image data, and it outputs a model that can pinpoint these anomalies without needing any prior examples of what an anomaly looks like. This is especially useful for those working with satellite imagery or sensor data where detecting rare occurrences is critical.
Use this if you need to detect unusual patterns or objects in hyperspectral images where you don't have labeled examples of what constitutes an 'anomaly'.
Not ideal if you are working with standard RGB images or if you already have a comprehensive dataset of known anomalies for supervised learning.
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Nov 11, 2025
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