التفاصيل البيبلوغرافية
| العنوان: |
SUPPORT VECTOR CLASSIFICATION OF LAND COVER AND BENTHIC HABITAT FROM HYPERSPECTRAL IMAGES. |
| المؤلفون: |
Manian, Vidya, Velez-Reyes, Miguel |
| المصدر: |
International Journal of High Speed Electronics & Systems; Jun2008, Vol. 18 Issue 2, p337-348, 12p, 3 Color Photographs, 2 Diagrams, 9 Charts, 1 Graph |
| مصطلحات موضوعية: |
SUPPORT vector machines, AIRBORNE Visible/Infrared Imaging Spectrometer (AVIRIS), BENTHIC plants, BENTHIC animals, IMAGING system design & construction, WAVELETS (Mathematics), COMPUTER software |
| مستخلص: |
This paper presents a novel wavelet and support vector machine (SVM) based method for hyperspectral image classification. A 1-D wavelet transform is applied to the pixel spectra, followed by feature extraction and SVM classification. Contrary to the traditional method of using pixel spectra with SVM classifier, our approach not only reduces the dimension of the input pixel feature vector but also improves the classification accuracy. Texture energy features computed in the spectral dimension are mapped using polynomial kernels and used for training the SVM classifier. Results with AVIRIS and other hyperspectral images for land cover and benthic habitat classification are presented. The accuracy of the method with limited training sets and computational burden is assessed. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
