Optimal scale selection for multi-scale segmentation based on RMNE method

doi:10.6046/gtzyyg.2019.02.02

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Abstract

Multi-scale segmentation is one of the most important methods in object oriented information extraction, and the selection of optimal segmentation scale is a hot topic. Nevertheless, existing optimal segmentation scale selection methods only use spectral characteristics. In view of such a situation, this paper proposes a RMNE method, which uses textural information entropy to measure the heterogeneity between objects, uses spectral characteristics mean difference to neighborhoods to measure the object’s internal homogeneity and construct the evaluation function, and selects the optimal segmentation scales by drawing function curve. Taking 6 m spatial resolution multi-spectral SPOT6 image of the periphery of Beijing City as the multi-scale segmentation experiment example, the authors detected that the optimal scales combination is 30, 60 and 80. Compared with the multi-scale segmentation results whose optimal scales are obtained by the maximum area method and objective function method, it is shown that the effect of RMNE method is the best, which verifies the validity of the RMNE method and the applicability of the high resolution image. A comparison with Google Earth image shows that the image object’s size obtained by RMNE method is most consistent with that of the actual ground object.

Keywords object oriented multi-scale segmentation RMNE optimal segmentation scale entropy of information SPOT6

TP79

Issue Date: 23 May 2019

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	Ning MAO
	Huiping LIU
	Xiangping LIU
	Yanghua ZHANG

Cite this article:

Ning MAO,Huiping LIU,Xiangping LIU, et al. Optimal scale selection for multi-scale segmentation based on RMNE method[J]. Remote Sensing for Land & Resources, 2019, 31(2): 10-16.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.02 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/10

Fig.1 Research flow chart in this paper

Fig.2 Remote sensing image of the study area

Fig.3 Result of RMNE method

Fig.4 Results of maximum area method and objective function method

Fig.5 Results of multi-resolution segmentation methods

Fig.6 Comparison of multi-scale segmentation results and reference images

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