Extraction of buildings in remote sensing imagery based on multi-level segmentation and classification hierarchical model and feature space optimization

doi:10.6046/gtzyyg.2019.03.15

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Abstract

In view of the problems of scale effect, spectral diversity and classification feature optimization in the extraction of urban objects information from high spatial resolution remote sensing images,the authors, based on the object-based image analysis method and combined with data mining and machine learning,propose a multi-level segmentation and classification hierarchical model and its feature space optimization method for building extraction. First, according to the multi-scale characteristics of remote sensing information, a hierarchical relationship is set up for the difference of features of ground objects, and then a hierarchical structure based on information segmentation and classification is established based on the characteristics of spectral diversity to define the subtypes of ground objects. After that, the proposed Relief F-PSO combination feature selection method is used. Finally,on the basis of multiscale segmentation and feature optimization, the water surface distribution is obtained based on the random forest model, and finally the building information is extracted by the J48 decision tree algorithm. Experimental results show that the method can utilize a small number of image feature attributes to get high-precision building extraction results.

Keywords object-based image analysis buildings segmentation and classification hierarchical model feature selection

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Issue Date: 30 August 2019

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	Tao DANG
	Qi SONG
	Yong LIU
	Anjian XU
	Bo XU
	Honggang ZHANG

Cite this article:

Tao DANG,Qi SONG,Yong LIU, et al. Extraction of buildings in remote sensing imagery based on multi-level segmentation and classification hierarchical model and feature space optimization[J]. Remote Sensing for Land & Resources, 2019, 31(3): 111-122.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.15 OR https://www.gtzyyg.com/EN/Y2019/V31/I3/111

Tab.1 Hierarchical image segmentation classification structure

Fig.1 Relief F-PSO feature selection algorithm flow chart

Fig.2 Study area image with WorldView3 B3(R),B2(G),B1(B)

Fig.3 Relationship between ED2 value and scale

Fig.4 Study area building extraction segmentation hierarchical graph

Fig.5 Image segmentation results(partial)

Fig.6 Mean value and brightness box diagram of each band of typical features

Fig.7 Mean value diagram of each band of typical figure

Fig.8 Characteristic importance degree analysis chart

Fig.9 Characteristic analysis diagram of typical feature samples

Fig.10 Relationship between the overall accuracy and the Kappa coefficient and the number of features

Tab.2 Optimal feature sets for impervious classes

Fig.11 Impervious ground extraction results based on random forests

Tab.3 Optimal feature sets for building classes

Fig.12 Typical feature sample analysis

Tab.4 Feature extraction rule set

Fig.13 Final building extraction results

Fig.14 Building extraction results from different classification methods

Tab.5 Statistical comparison of data related from different classification methods

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