Abstract
In this study, segmentation of hyperspectral images which is a multidisciplinary subject was propesed using Dirichlet mixture models. Due to the computational complexity and high volume and dimensional nature of hiperspectral images, principal componenet analysis (PCA) and its kernelized version kernel PCA (KPCA) were used in dimension reduction stage. Pre-segmentation step was realized with a selected sub-sampled dataset from all data; then segmentation of whole scene is accomplished by support vector machines (SVMs) and k-nearest neighbors (k-NN) methods. Obtained results are evaluated with k-means and fuzcy c-means algorithms by power of spectral discrimination (PWSD) metrics.
Keywords
Hyperspectral imaging
Artificial intelligence
Pattern recognition (psychology)
Kernel (algebra)
Computer science
Image segmentation
Principal component analysis
Kernel principal component analysis
Segmentation
Support vector machine
Scale-space segmentation
Dimensionality reduction
Dirichlet distribution
Mathematics
Computer vision
Kernel method
Subject Areas
Remote-Sensing Image Classification
·Media Technology ·Physical Sciences
Spectroscopy and Chemometric Analyses
·Analytical Chemistry ·Physical Sciences
Remote Sensing and Land Use
·Atmospheric Science ·Physical Sciences
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Reduced inequalities 49%
Peace, Justice and strong institutions 46%
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