Abstract
A new spectralspatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.
| Original language | English |
|---|---|
| Article number | 4840429 |
| Pages (from-to) | 2973-2987 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 47 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2009 |
Bibliographical note
Funding Information: Manuscript received October 3, 2008; revised December 16, 2008. First published April 24, 2009; current version published July 23, 2009. This work was supported in part by the Marie Curie Research Training Network “HYPER-I-NET.” Y. Tarabalka is with the Faculty of Electrical and Computer Engineering, University of Iceland, 107 Reykjavik, Iceland and also with the GIPSA-Lab-Grenoble Institute of Technology, Domaine Universitaire, 38402 Saint-Martin-d’Hères Cedex, France (e-mail: [email protected]).Other keywords
- Clustering
- Hyperspectral images
- Majority vote
- Segmentation
- Spectral-spatial classification