Resources
Technical validation, benchmark studies, platform workflows, company updates, and educational resources from our engineering and data science teams.
Evaluating Deep Learning Spectral Unmixing From Pure Reference Spectra
A deep learning model trained only on synthetic mixtures — generated from pure reference spectra — outperforms classical solvers on four- and five-material mixtures across nine sensors. The benchmark: 325 real clay powder mixtures measured by lab spectrometers, pushbroom cameras, snapshot cameras, MWIR, and RGB.
Turning One Reference Spectrum Into Full-Scene Target Detection
See how a CNN-based single-spectrum detector trained on Clarity outperformed classical baselines on full-scene MUUFL target detection across multiple train-test scene pairs.
Metaspectral Deep Learning Model Achieves State-of-the-Art Performances on Toulouse Hyperspectral Dataset Benchmark
Here we sought to demonstrate the efficiency and predictive power of our pixel-wise supervised CNN classifier which is benchmarked against the established baseline.