Decision fusion of pixel-level and superpixel-level hyperspectral image classifiers

In this paper, a decision fusion of pixel-level and superpixel-level classifiers (DFPSC) for the HSI is proposed. First, the support vector machine based classification probability combined with the local spatial information is introduced to classify the HSI in a pixel-by-pixel manner. Then, the HSI is over-segmented into non-overlapping superpixels. Each superpixel contains spatially-connected and spectrally-similar pixels, which are assigned to the same label via joint sparse regularization. Finally, a guided map is generated based on the edge map and superpixel map, which is used to guide the fusion of both pixel-level and superpixel-level classification results. With the proposed decision fusion scheme, the classification results in homogeneous and structural areas can be better balanced, leading to the improvement of the overall classification accuracy. The experimental results demonstrate the superiority of the proposed method over some well-known classification methods.