MATLAB Codes for Common Hyperspectral Unmixing Methods

The following MATLAB codes implement common hyperspectral unmixing methods designed for preprocessing and analyzing hyperspectral data. The implementation includes these key processing stages:

1. Data Loading and Preprocessing: The code reads hyperspectral data based on file format specifications (such as ENVI, MATLAB .mat files, or TIFF formats) and performs essential preprocessing operations using functions like imread() or custom I/O routines. This stage includes noise reduction algorithms (e.g., wavelet denoising), wavelength calibration using spectral normalization functions, and radiometric correction procedures.

2. Feature Extraction and Selection: Depending on the specific unmixing methodology (such as linear spectral unmixing or nonlinear approaches), appropriate feature extraction techniques are implemented using functions like pca() for dimensionality reduction or nfindr() for endmember selection. Feature selection algorithms optimize spectral characteristics to enhance unmixing accuracy.

3. Model Construction and Solution: The code establishes mathematical models corresponding to selected unmixing methods - implementing linear mixture models with constrained least squares optimization using lsqlin(), or nonlinear approaches with iterative optimization algorithms. The solving process employs numerical methods to derive abundance fractions of mixed components.

4. Result Evaluation and Analysis: Evaluation metrics such as root mean square error (RMSE) and spectral angle mapper (SAM) are calculated to assess unmixing performance. Post-processing functions analyze abundance maps and generate visualization outputs using MATLAB's image processing toolbox for result interpretation.

These implemented methods enable researchers to better understand hyperspectral data characteristics and perform more precise analytical processing through systematic algorithm implementation.