A combined object-based segmentation and support vector machines approach for classification of Tiangong-1 hyperspectral image

Abstract

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 fusing object-based segmentation with pixel-wise classifier. This paper tentatively examines 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-scalewatershed segmentation with Support Vector Machine (WSVM). The two methods were applied to Tiangong-1 hyperspectral data and the results showed that the proposed methods improved the classification accuracy effectively which not only avoid the spectral confusion to some extent but also mitigate the land fragmentation problem.