Principal Component Analysis for Hyperspectral Images

This documentation presents a compact program for performing Principal Component Analysis on hyperspectral images, with the requirement that input data must be in .mat format. The implementation utilizes MATLAB's built-in PCA functions or custom eigenvalue decomposition algorithms to handle high-dimensional spectral data efficiently. The program typically involves data preprocessing steps like normalization, covariance matrix computation, and eigenvector extraction to transform original spectral bands into principal components. We can enhance this discussion by including the mathematical background of PCA and its purpose in reducing dimensionality while preserving critical spectral information. The importance of .mat format lies in its efficient storage of multidimensional arrays and metadata, which is crucial for handling large hyperspectral datasets. From an implementation perspective, the program might include functions for loading .mat files using MATLAB's load() command, data validation checks, and visualization of principal components. Practical examples could demonstrate how to analyze spectral signatures across different land cover types, showing the program's effectiveness in feature extraction and noise reduction. Potential enhancements could include adding support for different file formats through conversion utilities, implementing incremental PCA for large datasets, incorporating automated component selection criteria, or adding classification algorithms that leverage the reduced-dimensional features for improved hyperspectral image analysis applications.