Anomaly-Detection: Find outliers
Detects anomalies in hyperspectral images using a low-rank and sparse representation algorithm
- •You have a large dataset of hyperspectral images and need to identify unusual patterns or objects
- •You are working in a field such as environmental monitoring, agricultural surveying, or mineral exploration where hyperspectral imaging is used
- •You want to automate the process of anomaly detection in hyperspectral images to improve efficiency and accuracy
Run the command `ADLRSR('input_image.mat', 'output_image.mat', 0.1, 0.5)` to detect anomalies in the `input_image.mat` file with a low-rank threshold of 0.1 and a sparse threshold of 0.5Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank && curl -fsSL https://workflowstacks.com/api/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank/claude-skill -o ~/.claude/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank && curl -fsSL https://workflowstacks.com/api/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank/claude-skill -o ~/.claude/skills/anomaly-detection-in-hyperspectral-images-based-on-low-rank/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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