Custom PCA Function for Hyperspectral Image Processing

In hyperspectral image processing, our custom Principal Component Analysis (PCA) function operates within the MATLAB environment to perform dimensionality reduction on high-dimensional spectral data. The implementation typically involves covariance matrix computation, eigenvalue decomposition, and projection of original data onto principal component axes. This dimensionality reduction technique significantly reduces computational complexity by transforming correlated spectral bands into orthogonal components, thereby improving processing efficiency while preserving essential spectral information. The modular code architecture allows researchers to customize parameters such as the number of retained components, data normalization methods, and covariance calculation approaches based on specific application requirements. Consequently, employing this tailored PCA function enhances data handling capabilities and optimizes processing workflows for hyperspectral imagery analysis.