Data Analysis Tools =================== This section helps you understand how to use WISER's data analysis tools — the algorithms for transforming, classifying, detecting, unmixing, and visualizing hyperspectral imagery. Each page explains what the tool does, how it works, and how to run it from the interface. Transforms & Dimensionality Reduction -------------------------------------- - :doc:`Principal Component Analysis (PCA) ` — reduce a cube to a few bands ordered by variance. - :doc:`Minimum Noise Fraction (MNF) ` — reduce a cube to bands ordered by signal-to-noise ratio. Classification & Clustering --------------------------- - :doc:`K-Means Clustering ` — group pixels into clusters by spectral similarity. Target Detection & Spectral Matching ------------------------------------ - :doc:`Mixture-Tuned Matched Filter (MTMF) ` — detect a target material against an unknown background. - :doc:`Spectral Angle Mapper (SAM) ` — match pixels to reference spectra by spectral angle. - :doc:`Spectral Feature Fitting (SFF) ` — match pixels to reference spectra by their absorption features. Unmixing -------- - :doc:`Linear Unmixing ` — estimate per-pixel abundances of a set of endmember spectra. Visualization ------------- - :doc:`Interactive Scatter Plot ` — explore two bands in feature space and link selections back to the image. .. toctree:: :hidden: pca mnf kmeans mtmf spectral-angle-mapper spectral-feature-fitting linear-unmixing interactive-scatter-plot