Acta Optica Sinica, Volume. 36, Issue 10, 1028003(2016)
Hyperspectral Image Classification Algorithm Based on Gabor Feature and Locality-Preserving Dimensionality Reduction
Two hyperspectral image classification algorithms based on Gabor features and locality-preserving dimensionality reduction are proposed. The Gabor transform is studied and implemented to extract features for hyperspectral image in the principal component analysis-projected domain. To protect locality information of neighbor features, locality Fisher discriminant analysis or locality-preserving non-negative matrix factorization is employed to reduce the dimensionality of Gabor-based feature space. The Gaussian mixture model classifier is used for classification results. Experimental results obtained from two hyperspectral datasets show that the proposed algorithms not only extract spectral-spatial features effectively, but also preserve local-feature information and multi-model structure of hyperspectral image. Compared with several existing algorithms, the proposed algorithms can obtain high classification accuracy and Kappa coefficient, and has strong robustness in Gaussian noise environment.
Get Citation
Copy Citation Text
Ye Zhen, Bai Lin, Nian Yongjian. Hyperspectral Image Classification Algorithm Based on Gabor Feature and Locality-Preserving Dimensionality Reduction[J]. Acta Optica Sinica, 2016, 36(10): 1028003
Category: Remote Sensing and Sensors
Received: Apr. 21, 2016
Accepted: --
Published Online: Oct. 12, 2016
The Author Email: Zhen Ye (yezhen525@126.com)