Hyperspectral Image Dimensionality Reduction Using Inter-Band Cross-Correlation and K-Means Clustering Algorithm

• Published in: International Workshops on Image Processing Theory, Tools, and Applications pp. 01 - 06
• Main Authors: Ndu, Henry, Sheikh-Akbari, Akbar, Singh, Koushlendra K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.10.2024
• Subjects: Accuracy; Clustering algorithms; Correlation; Dimensionality reduction; Eigenvalues and eigenfunctions; Hyperspectral imaging; inter-band correlation; Medical diagnosis; Morphological operations; PCA; Principal component analysis; Support vector machines; SVM; vein detection
• ISSN: 2154-512X
• DOI: 10.1109/IPTA62886.2024.10755689

Hyperspectral imaging has become crucial in various domains, especially for the accurate detection of human veins in medical diagnostics, though managing the extensive data from hyperspectral (HS) images remains a challenge. To improve data handling during analysis, dimensionality reduction methods are frequently utilized. This paper presents a dimensionality reduction method for HS images using HS image inter-band cross-correlation and the K-means clustering algorithm. The proposed method computes inter-band correlations across all bands of the input HS image, which form a 2D correlation matrix. Eigen-decomposition is applied to the resulting matrix, extracting its eigenvectors and eigenvalues. The k-mean clustering algorithm is then applied to a selection of eigenvectors representing the largest eigenvalues, splitting the eigenvectors into several clusters. The reduced HS image is generated by averaging each cluster's image bands. The proposed dimensionality reduction method together with the Support Vector Machine (SVM) classifier was then used for vein detection in HS images. The HyperVein image dataset was used to generate experimental results. Experimental results were generated for the proposed method and Principal Component Analysis (PCA) and Folded PCA (FPCA). Results show the proposed method outperforms PCA and FPCA in most performance metrics.