Automatic building detection of high-resolution remote sensing images based on multi-scale and multi-feature

doi:10.6046/gtzyyg.2019.01.10

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Abstract

Building detection plays an important role in urban planning, change detection, surface coverage and so on. However, in high resolution remote sensing images, buildings vary in shape, color, and size, which makes building detection a difficult problem. Therefore, this paper proposes a method based on multi-scale and multi-feature to automatically extract buildings in high resolution images: Firstly, down sampling images are used to construct Gauss pyramid model, while fixed size windows in different layers of pyramid image represent different ground areas. Then multi features are calculated which describe building characteristics by sliding windows, and multi features are fused to evaluate the saliency of building in different scales. Then the saliency of superpixels is calculated, and Otsu algorithm is used to automatically determine the threshold, and furthermore, some constraints such as the aspect ratio were combined to extract buildings accurately and automatically. Experiments were made by 0.5 m and 0.2 m high resolution remote sensing images in comparison with the markov random field model based on color and texture modeling algorithm for qualitative and quantitative comparison. The results show that the method suggested in this paper can obtain more satisfactory precision and has higher effect on building detection from high-resolution remote sensing images.

Keywords high resolution image multi-scale multi-feature building detection superpixel

TP751.1

Corresponding Authors: Xiangyun HU E-mail: huxy@whu.edu.cn

Issue Date: 15 March 2019

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	Liuqing WU
	Xiangyun HU

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Liuqing WU,Xiangyun HU. Automatic building detection of high-resolution remote sensing images based on multi-scale and multi-feature[J]. Remote Sensing for Land & Resources, 2019, 31(1): 71-78.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.10 OR https://www.gtzyyg.com/EN/Y2019/V31/I1/71

Fig.1 Flow chart of proposed algorithm

Fig.2 Sketch map of multi-scale building detection by Gauss pyramid images

Fig.3 Distribution of edge points in four quadrants

Fig.4 Orthogonality of main direction of building and histogram of distribution of edge points at 12 direction bins

Fig.5 Square template with dark light around

Fig.6 Multi-scale and multi-feature fusion computing saliency map

Fig.7 Detection results and reference results of experiment 1 images

Tab.1 Comparison of detection accuracy between two algorithms of experiment 1 (%)

Fig.8 Detection result of experiment 2 image

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