Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering

The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectralspatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyper-spectral image is classified using a pixel-wise classifier, e.g., the support vector machines classifier. Then, the resulting classification map is represented as multiple probability maps and edge-preserving filtering is conducted on each probability map with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color reference image. Finally, maximum selection is performed on the filtered probabilities to obtain the final classification result. Experimental results demonstrate that the proposed edge-preserving filtering (EPF) based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.