Superpixel spectral features-based automatic fuzzy clustering segmentation for UAV image

doi:10.11947/j.AGCS.2022.20210151

Abstract

Abstract: Aiming at the problems of the existing popular fuzzy C-means clustering in image segmentation, such as the weak boundary attachment ability, the unstable segmentation process and the need to manually set the number of clusters, a super-pixel spectral features-based automatic fuzzy clustering segmentation for UAV image is proposed. Firstly, the watershed-based super-pixels algorithm with boundary advancing criterions are used to generate boundary adherent and compact super-pixels. Then extract the spectral features of super-pixels, and obtain the cluster number is automatically by rescaled density peak algorithm. Finally, an improved FCM method combining spectral features and hidden Markov random field is adopt to achieve high-precision super-pixels merging. Through qualitative analysis and quantitative evaluation, the results show that the proposed method can accurately locate the target boundary, obtain the optimal segmentation results and effectively improve the image segmentation accuracy.

Key words: UAV image, fuzzy clustering, super-pixel, boundary advancing criterions, rescaled density peak algorithm, hidden Markov random field

CLC Number:

P227

TANG Xiaofang, ZHAN Zongqian, DING Jiujie, LIU Jiahui, XIONG Zirou. Superpixel spectral features-based automatic fuzzy clustering segmentation for UAV image[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(5): 677-690.

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