PARACUDA-NG
Spectral machine learning: build, validate and apply regression models for spectroscopy and multispectral / hyperspectral imagery, integrated with QGIS raster layers.
PARACUDA-NG (PARAmetric CUbe Data Analysis, Next Generation) is a spectral machine-learning toolkit for spectroscopy and multispectral / hyperspectral remote sensing, embedded in QGIS as a native, dockable panel. A 7-step wizard (Data, Configuration, Preprocess, Model, Validate, Execution, Apply) takes you from loading spectral data through spectral resampling to a selectable sensor grid (Sentinel-2, Landsat 5/7/8/9, EnMAP, EMIT, PRISMA, DESIS, PlanetScope, VENuS and more), preprocessing (smoothing, continuum removal, derivatives, absorbance, baseline correction), and training or comparing regression models (PLS-R, SVM, Ridge, Lasso, MLR, Elastic Net, Huber, Gradient Boosting, Gaussian Process, Random Forest, XGBoost) with optional Optuna hyperparameter tuning and cross-validation. A trained model can be applied directly to a loaded QGIS raster layer, writing the prediction back as a new layer with a matching colour map, or to an unknown tabular dataset. Additional tools include compositional (CLR/ALR/ILR) modelling for parts that sum to 100 percent, a label-permutation and spectral-mixing integrity suite, cross-sensor spectral harmonisation (transfer functions), and a data converter for arbitrary instrument layouts. PARACUDA-NG needs a few Python packages QGIS does not ship (scikit-learn, joblib, optuna, xgboost, rasterio, spectral); it installs them into QGIS's Python on first run, or via Tools then Install Python dependencies.