Abstract
In this work, segmentation of hyperspectral images by local covariance matrices in eigenspace has been proposed for getting high accuracy rates using unsupervised methods. Combination of both spectral and spatial features can increase the segmentation accuracy for hyperspectral images without groundtruth. Furthermore, changing from original data space to eigenspace via principal component analysis and its kernelized version and the calculation of covariance matrices in this new space can produce better results for different clustering methods. In the simulations, effects of local neighbors in the computation of covariance matrices in eigenspace were represented using four different clustering algorithms comparatively.
Keywords
Hyperspectral imaging
Covariance
Pattern recognition (psychology)
Principal component analysis
Cluster analysis
Artificial intelligence
Eigenvalues and eigenvectors
Covariance matrix
Spectral clustering
Mathematics
Computation
Computer science
Segmentation
Algorithm
Statistics
Subject Areas
Remote-Sensing Image Classification
·Media Technology ·Physical Sciences
Remote Sensing and Land Use
·Atmospheric Science ·Physical Sciences
Image Retrieval and Classification Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
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