Name
Abstract | ||
|---|---|---|
In this study, segmentation of hyperspectral images which is a multidisciplinary subject was proposed using Dirichlet mixture models. Due to the computational complexity and high volume and dimensional nature of hyperspectral images, principal component 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 fuzzy c-means algorithms by power of spectral discrimination (PWSD) metrics. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SIU.2014.6830396 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
computational complexity,fuzzy systems,hyperspectral imaging,image segmentation,mixture models,principal component analysis,support vector machines,Dirichlet mixture models,KPCA,SVM,computational complexity,dimension reduction,fuzzy c-means algorithms,hyperspectral image segmentation,k-NN method,k-means algorithms,k-nearest neighbors method,kernelized version kernel,multidisciplinary subject,principal component analysis,spectral discrimination,sub-sampled dataset,support vector machines,Dirichlet mixture model,clustering,hyperspectral images,power of spectral discrimination,segmentation | Computer vision,Scale-space segmentation,Dimensionality reduction,Pattern recognition,Computer science,Support vector machine,Segmentation-based object categorization,Kernel principal component analysis,Image segmentation,Hyperspectral imaging,Artificial intelligence,Mixture model | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Onur Sigirci | 1 | 1 | 1.39 |
| Gökhan Bilgin | 2 | 62 | 13.18 |