MATLAB Programs for Hyperspectral Image Processing

This MATLAB program suite is designed for hyperspectral image processing, incorporating three core functionalities: image fusion, dimensionality reduction, and maximum likelihood classification. Image fusion involves combining multiple spectral images into a comprehensive composite image to extract enhanced useful information through techniques like principal component analysis or wavelet-based fusion methods. Dimensionality reduction simplifies hyperspectral data by reducing feature dimensions using algorithms such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA), making subsequent analysis more efficient. The maximum likelihood classification program implements a statistical model-based algorithm that calculates probability distributions for different classes, enabling accurate pixel-wise classification and pattern recognition based on spectral characteristics and data distribution patterns. The implementation typically involves covariance matrix calculations and probability density function estimations for optimal class separation.