Hyperspectral image segmentation method based on spatial-spectral constrained region active contour

Hyperspectral image provides rich information of ground covers, which has been brought great attention. Combining hyperspectral spatial and spectral constraint, an image segmentation method based on region active contour is proposed in this paper. The energy function in Chan-Vese´s method is improved and both spatial and spectral information are employed. Spatial term of the function is restricted by global spatial information and intensity consistency in homogeneous region, while spectral term is restricted by spectral similarity of the target. Finally the image is segmented by minimizing the energy function. In order to verify the effectiveness of the proposed method, experiments are conducted on AVIRIS data. Results show that the method can extract indistinct contours of interested target in hyperspectral image, and reduce the influence caused by mixed pixels and the shadows around the target, and also adapts to complex scene in some degree.