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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ghamisi, P. Benediktsson, J.A. Ulfarsson, M.O. |
| Copyright Year | 1980 |
| Abstract | Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependences, which leads to a dramatic decrease in classification accuracies. In this paper, a new automatic framework for the classification of hyperspectral images is proposed. The new method is based on combining hidden Markov random field segmentation with support vector machine (SVM) classifier. In order to preserve edges in the final classification map, a gradient step is taken into account. Experiments confirm that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies compared to the standard SVM method and also outperforms other studied methods. |
| Sponsorship | IEEE Geoscience and Remote Sensing Society IEEE URSI |
| Starting Page | 2565 |
| Ending Page | 2574 |
| Page Count | 10 |
| File Size | 1742668 |
| File Format | |
| ISSN | 01962892 |
| Volume Number | 52 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | support vector machine (SVM) classifier Hidden Markov random field (HMRF) hyperspectral image analysis image segmentation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Electrical and Electronic Engineering |
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