Principal Component Analysis (PCA)

PCA reduces a hyperspectral cube to a smaller set of bands ordered by variance: the first principal component captures the most variation in the data, the second the next most, and so on. Bands deemed bad are dropped before the fit, so only good bands contribute.

How it works

The covariance matrix of the (good-band) spectra is eigendecomposed. Each eigenvector is a principal component — a direction in spectral space — and its eigenvalue is the variance the data has along that direction. The cube is projected onto the top N eigenvectors (largest eigenvalues), producing a new dataset with N PCA bands ordered from most to least variance.

The result is added as a new dataset named PCA on <source>.

Using the tool

Right-click a raster image and choose PCA. In the dialog, set Number of Components (how many PCA bands to keep) and click OK. The spin box defaults to the maximum — the count of good bands. Estimator Matrix offers only Covariance and is disabled.

When the run finishes, a PCA metadata widget and a scree plot (eigenvalue vs. component index) open automatically. Click View Past Results to reopen the scree plot for any earlier run; it helps you judge how many components are worth keeping.