Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification


A 3-D Gabor wavelet provides an effective way to obtain the spectral-spatial-fused features for hyperspectral image, which has shown advantageous performance for material classification and recognition. In this paper, instead of separately employing the Gabor magnitude and phase features, which, respectively, reflect the intensity and variation of surface materials in local area, a cascade superpixel regularized Gabor feature fusion (CSRGFF) approach has been proposed. First, the Gabor filters with particular orientation are utilized to obtain Gabor features (including magnitude and phase) from the original hyperspectral image. Second, a support vector machine (SVM)-based probability representation strategy is developed to fully exploit the decision information in SVM output, and the achieved confidence score can make the following fusion with Gabor phase more effective. Meanwhile, the quadrant bit coding and Hamming distance metric are applied to encode the Gabor phase features and measure sample similarity in sequence. Third, the carefully defined characteristics of two kinds of features are directly combined together without any weighting operation to describe the weight of samples belonging to each class. Finally, a series of superpixel graphs extracted from the raw hyperspectral image with different numbers of superpixels are employed to successively regularize the weighting cube from over-segmentation to under-segmentation, and the classification performance gradually improves with the decrease in the number of superpixels in the regularization procedure. Four widely used real hyperspectral images have been conducted, and the experimental results constantly demonstrate the superiority of our CSRGFF approach over several state-of-the-art methods.

In IEEE Transactions on Neural Networks and Learning Systems
Bin Deng
Bin Deng
Ph.D Student

My research interests include hyperspectral image processing, pattern recognition and machine learning.