Spectral-spatial conditional random field classifier with location cues for high spatial resolution imagery

In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are mainly provided by support vector machine (SVM), because of its excellent spectral classification performance. However, it is difficult to deal with the common spectral variability problem in remote sensing images. To alleviate this dilemma, considering the spectral similarity of the same land-cover in a local region, a point-to-point (P2P) classifier is designed to emphasize the spatial location cues. The P2P classifier considers the nonlocal range of the spatial location interactions between the target pixel and its nearest training samples for all the classes. In addition, the pairwise potential of CRFSS also considers the spatial contextual information to favor spatial smoothing. The experimental results showed that the algorithm has a competitive classification performance, in both the quantitative and qualitative evaluation.