Yan Pengfei, Ming Dongping
Segmentation of high spatial resolution remotely sensed image is the important foundation of Object\|Based Image Analysis(OBIA), most of the image segmentation algorithms involve the problem of parameter setting. Self\|adaptive Parameterization is one of the key factors that affect the efficiency and effectiveness of remote sensing image segmentation. Considering that traditional watershed segmentation algorithm is susceptible to noise and the segmentation scale parameter is difficult to be self\|adaptively chosen,this paper propose a scale self\|adaptive method in watershed segmentation. After median filtering in primary image, this paper uses spatial statistical method to realize the self\|adaptive setting of watershed segmentation parameters, and then segments the high spatial resolution remote sensing image. This study uses IKONOS and Quickbird multispectral images as experimental data to testify the validity of the method proposed by this paper. The homogeneity within the segmentation parcels and the heterogeneity between thesegmentation parcels are used to build up a synthetic evaluation model to quantitatively evaluate the segmentation results by the proposed method by comparing with different parameter sequences segmentation results. The comparison result show that the proposed method perform well in high spatial resolution image segmentation. As result, the method proposed in this paper not only improves the accuracy of image segmentation to a certain extent, but also raises the automation of the segmentation parameter selection, which provides a new way for image segmentation and the research of parameterization in the future.
