Abstract:

Hyperspectral image has great potential in the classification and recognization of different objects, whose inherent physical and chemical properties can be reflected by the spectral features of the image bands. In order to overcome the high redundancy among the large number of bands of hyperspectral image, efficient dimensional reduction algorithms should be applied to improve the performance of image classification. In this paper, we present a modified manifold learning algorithm termed discriminative locality alignment (DLA) for the dimensional reduction of Hypersion image data. The proposed method transformed the original spectral feature space into the optimal low dimensional subspace by imposing discriminative information which from given raining samples in the manifold learning framework. In this subspace, the maximum likelihood classifier was then used to classify each pixel of the Hypersion image.Meanwhile,the classifycation results based on the dimensional reduction algorithms of principle component analysis (PCA) and original spectral were compared with the performance of classification based on DLA. The experiments showed that DLA can effectively improve the classification accuracy of hyperspectral image data, and obtained satisfactory classification results for tree species.

Key words: manifold learning, hyperspectral, dimension reduction, classification