High-resolution remote sensing image segmentation using minimum spanning tree tessellation and RHMRF-FCM algorithm

doi:10.11947/j.AGCS.2019.20170585

Abstract

Abstract:

It is proposed that a high-resolution remote sensing image segmentation method that combines static minimum spanning tree tessellation considering shape information and the RHMRF-FCM algorithm. It solves the problems in traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in complex boundary exist. By using the MST model and shape information, the object boundary and geometrical noise can be expressed and reduced respectively. Firstly, the static MST tessellation is employed for partitioning the image domain into some polygons corresponded to the components of homogeneous regions needed to be segmented. Secondly, based on the tessellation results, the RHMRF model is built, and regulation term considering the KL information and information entropy are introduced into the FCM objective function. Finally, the partial differential method is employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results. To verify the robust and effective of proposed algorithm, the experiments are carried out with WorldView-3 high resolution image. The results from proposed method with different parameters and comparing methods (the multi-resolution and the watershed segmentation method in eCognition software) are analyzed qualitatively and quantitatively.

Key words: static minimum spanning tree tessellation, shape parameter, regional hidden Markov random field, fuzzy c-means algorithm, high-resolution remote sensing image segmentation

CLC Number:

P237

LIN Wenjie, LI Yu, ZHAO Quanhua. High-resolution remote sensing image segmentation using minimum spanning tree tessellation and RHMRF-FCM algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 64-74.

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