Research Article
3D Gray-Gradient-Gradient Tensor Field Feature For Hyperspectral Image Classification
@INPROCEEDINGS{10.4108/eai.15-8-2015.2260615, author={Zhaojun Wu and Qiang Wang and Yi Shen}, title={3D Gray-Gradient-Gradient Tensor Field Feature For Hyperspectral Image Classification}, proceedings={10th EAI International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={9}, keywords={hyperspectral image classification three dimensional texture analysis gray-gradient-gradient tensor field spectral derivative features}, doi={10.4108/eai.15-8-2015.2260615} }- Zhaojun Wu
Qiang Wang
Yi Shen
Year: 2015
3D Gray-Gradient-Gradient Tensor Field Feature For Hyperspectral Image Classification
CHINACOM
IEEE
DOI: 10.4108/eai.15-8-2015.2260615
Abstract
The texture feature is an important information for hyperspectral image classification. In this study, we extend the traditional 2D GLGCM(gray-level gradient cooccurrence matrix) into 3D GGGTF(gray-gradient-gradient tensor field), which can extract gray and gradient texture features of hyperspectral volume data simultaneously. A few statistical features are extended into third-order forms in order to calculate texture properties of the generated GGGTF. And then, the extracted texture features are classified by linear polynomial kernel SVM classifier. Two widely used hyperspectral datasets are used to test the performance of the proposed GGGTF. Experimental results demonstrate that it outperforms traditional 2D GLGCM method in feature extraction for supervised classifications.