A combined object-based segmentation and support vector machines approach for classification of Tiangong-01 hyperspectral urban data

Traditional hyperspectral classification methods based on per-pixel spectral or texture features fail to take account of spatial structure and spatial correlation characteristics. In order to overcome this problem, a mixed classification method is proposed which incorporates spatial information by fusion of object-based segmentation with pixel-wise classifier. This paper tentatively assesses two mixed classification strategies: (1) Combine multi-resolution segmentation algorithm which based on Fractal Net Evolution Approach with the use of Support Vector Machine (MSVM); (2) Combine multi-scale watershed segmentation with Support Vector Machine (WSVM). The two methods were applied to Tiangong-01 hyperspectral urban data and the results showed that the proposed methods improve the classification accuracy effectively which not only avoid the spectral confusion to some extent but also mitigate the land fragmentation problem.