Hyperspectral Imaging API Library

Calibrate hyperspectral cubes, normalize reflectance, remove noise and prepare datasets for scientific analysis.

Identify informative wavelength bands for dimensionality reduction and improved spectral model performance.

Classify pixels, regions and materials using machine learning and spectral feature extraction pipelines.

Improve spectral image quality using denoising, correction, enhancement and restoration methods.

Segment crops, tissues, minerals and surfaces using AI-assisted spatial-spectral image analysis.

Detect abnormal spectral patterns, contamination, defects and rare material signatures in image cubes.

Match spectral signatures against reference libraries for materials, minerals and biological samples.

Generate 3D models and scientific visualizations from spectral and image-derived data.

Analyze crop stress, disease indicators, vegetation indices, soil status and plant health patterns.

Process airborne and satellite hyperspectral data for land cover and environmental monitoring.

Create structured outputs, spectral plots, metrics and reports for research documentation.

Annotate UAV-captured human detection datasets with bounding boxes, object labels, geotags and structured training metadata.

Apply quantum-inspired image enhancement, feature extraction, noise filtering and spectral image refinement for advanced research workflows.

Customize, automate and extract data from thermal imaging workflows, including frame processing, thermal region detection and sensor-based analytics.

Perform geospatial visualization, UAV image mapping, feature detection and spatial object localization from aerial imagery.

Convert drone imagery into georeferenced maps, orthomosaic outputs, terrain models and 3D reconstruction datasets.