Reading Hyperspectral Remote Sensing Images and Performing Principal Component Analysis
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In this paper, we developed a MATLAB program to read hyperspectral remote sensing images and perform principal component analysis (PCA). The implementation includes loading multidimensional image data using functions like multibandread or imread, followed by data normalization and covariance matrix calculation. The PCA algorithm computes eigenvalues and eigenvectors through singular value decomposition (svd function), with results sorted by contribution rate using sort function and visualized through imagesc or montage displays. Additional features include histogram-based image enhancement using histeq and wavelet-based denoising (wdenoise) to improve output quality. Our program provides an effective methodology for analyzing and processing hyperspectral imagery, facilitating better understanding of natural environments and resources through quantitative spectral dimension reduction.
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