PCA-Based Remote Sensing Image Fusion

This article presents a MATLAB implementation of Principal Component Analysis (PCA)-based remote sensing image fusion. The program, thoroughly tested on MATLAB 2011b, provides an effective tool for integrating multiple spectral bands of remote sensing imagery to produce higher-resolution and more accurate composite images. The implementation employs PCA transformation to extract principal components from input bands, followed by histogram matching and component substitution techniques to preserve spectral characteristics while enhancing spatial details. This solution finds extensive applications in Geographic Information Systems (GIS) and remote sensing image processing, enabling researchers and engineers to better analyze natural and artificial environmental features. Key MATLAB functions involved include pca() for dimensionality reduction, imhistmatch() for spectral consistency, and matrix operations for component fusion. Professionals in GIS and remote sensing fields are encouraged to master this practical tool for advanced image analysis tasks.