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SAR Image Connected Component Extractor
x402
SAR Image Connected Component Extractor is a paid API for AI agents from space.halowerk.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-09-14).
Labels four-neighbor connected components in a 2-D numeric grid above a caller-defined threshold, returning area, bounding box, peak, and mean value for each component.
Price / call$0.004
Success—
StatusUnknown
How this service works
Treats cells at or above a caller threshold as foreground, labels four-neighbor connected components, and reports area, bounding box, peak, and mean value. It does not ingest imagery, calibrate backscatter, remove speckle, geolocate pixels, classify objects, or infer real-world meaning.
Output
A list of detected foreground connected components, each with: pixel count (area), bounding box coordinates, peak cell value, and mean cell value across the component. Components below the minimum pixel threshold are excluded.
Example use cases
Detect ship-sized objects in SAR scene
“I have a 128×128 SAR intensity grid and want to find all connected clusters of pixels above a backscatter threshold of 200, with each pixel covering 25 square meters — only keep components of at least 10 pixels and tell me the bounding box and area of each detected object.”
Measure flood extent from radar grid
“Here's a 100×100 numeric grid of radar return values from a flood mapping pass. Set the foreground threshold to 80, pixel area to 400 square meters, and a minimum size of 3 pixels — I need the area and mean value of each flooded region.”
Extract bright anomaly clusters for change detection
“Run connected component analysis on this 64×64 backscatter difference grid with a threshold of 50 and pixel area of 900 square meters, ignoring blobs smaller than 2 pixels, so I can see where significant changes occurred and how large each anomaly cluster is.”
Technical details
Endpoint
POSThttps://space.halowerk.com/v1/sar-image-extractWhen to prefer
Use this endpoint when you have a raw 2-D numeric grid (e.g. SAR backscatter, difference images, or any raster) and need fast connected-component labeling with per-component statistics. It is ideal for detecting and measuring discrete foreground blobs by threshold without needing image ingestion, geolocation, speckle filtering, or object classification — those must be handled upstream or downstream.
Side effects
- Grid dimensions exceed 128×128 limit — request rejected
- Threshold set so high no foreground pixels exist — empty component list returned
- Minimum component pixel size filters out all detected regions — empty result
- Grid contains non-numeric or out-of-range values — validation error
- Malformed grid (ragged rows, non-array types) — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| grid | array | |
| threshold | number | |
| pixel_area_m2 | number | |
| minimum_component_pixels | integer |
Tags
x402
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