Abstract: Hyperspectral remote sensing image has the characteristics of rich spectral information and combining image with spectrum, which has been widely applied in the earth observation. Most of traditional hyperspectral image classification models don't make fully use of spatial feature information, rely too much on the spectral imformation, making the classification accuracy still have a lot of room to improve. Conditional random field (CRF) is a kind of probability mode that can better integrate spatial context information. It plays a more and more important role in hyperspectral image classification. However, most CRF models have the problem of excess smoothness, which will result in the loss of detail information. Aiming at this problem, this paper proposed a hyperspectral image classification method based on space-spectral fusion conditional random field. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to consider the spatial feature information and retain the details in each class. The experiments on two sets of hyperspectral image showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information.

Key words: hyperspectral remote sensing imagery, conditional random field, space-spectral fusion, image classification