SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH GAUSSIAN PROCESS

In this paper, a spectral-spatial classification method with Gaussian process was proposed for hyperspectral image classification. This method exploits the relationship among adjacent pixels and integrates it into spectral information to obtain spectral-spatial classification. In the proposed approach, the spatial information of a single pixel is weighted by the cosine similarity value between the adjacent pixels in the neighborhood. Experiments were conducted on the AVIRIS Indian Pines data set to evaluate the performance of the proposed approach. And the results demonstrated the effectiveness of the proposed methods to improve the classification performance by consideration of the spatial relationship between adjacent pixels in the hyperspectral image.