DegangWang97/IEEE_JSTARS_NL2Net
[JSTARS 2025 ESI Highly Cited Paper (TOP 1%)] Non-Local and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection
This project helps remote sensing analysts or environmental scientists automatically pinpoint unusual objects or phenomena in hyperspectral satellite images. It takes raw hyperspectral image data as input and outputs a map highlighting these anomalies, without needing prior knowledge about what to look for. This is ideal for professionals monitoring vast areas for subtle changes.
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Use this if you need to detect targets that deviate significantly from their surroundings in hyperspectral imagery, such as environmental contamination, undocumented infrastructure, or unusual geological formations.
Not ideal if you need to classify known objects in hyperspectral images or if you have limited computational resources for deep learning models.
Stars
11
Forks
2
Language
Python
License
GPL-2.0
Category
Last pushed
Mar 03, 2025
Commits (30d)
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