High resolution remote sensing image segmentation using super-pixel MRF for agricultural area

doi:10.6046/gtzyyg.2018.01.06

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Abstract

In view of the problem that the traditional super-pixel Markov random field (MRF) image segmentation model cannot fully utilize spatial context information, a new super-pixel MRF model is proposed. This algorithm incorporates higher-order neighborhood model into the interactive potential term of MRF. The new model enables the interactive potential to fully exploit the spatial context information contained in the super-pixel neighborhood system. Additionally, a new class-wise estimation method for β is proposed, which is based on norm distance. By utilizing two scenes of high-resolution remote sensing images acquired over different agricultural landscapes, validation experiment was conducted. The experiment results indicate that the proposed method can better use the contextual information such as edge strength, thus achieving higher segmentation accuracy. Moreover, the algorithm proposed by the authors showed superior performance when it was compared with other super-pixel MRF approaches.

Keywords super-pixel Markov random field (MRF) higher-order neighborhood agricultural area

TP79

Issue Date: 08 February 2018

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	Tengfei SU
	Shengwei ZHANG
	Hongyu LI

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Tengfei SU,Shengwei ZHANG,Hongyu LI. High resolution remote sensing image segmentation using super-pixel MRF for agricultural area[J]. Remote Sensing for Land & Resources, 2018, 30(1): 37-44.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.06 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/37

Fig.1 Illustration of super-pixels’ boundary

Tab.1 Basic information of the image data used for experiment

Fig.2 Subsets for experiment

Fig.3 Relationship between the performance of the proposed algorithm and h_n

Fig.4 Super-pixel segmentation results and edge strength extraction results of S1 and S2 subsets

Fig.5 S1 subset segmentation results of 4 different MRF algorithms

Fig.6 S2 subset segmentation results of 4 different MRF algorithms

Tab.2 Quantitative evaluation of segmentation accuracy by 4 algorithms for S1 and S2 subsets

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