A one-class classification by spatial-contextual for remotely sensed image

Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. When classifying hyperspectral and multispectral images with the existing algorithms, we use only the spectral information more often. This paper presents an one-class classification techniques, which is based spatial-contextual term, this study modifies the decision function and constraints of support vector data description. Experimental results show that the proposed method achieves good classification performance on hyperspectral image.