Multiple-scale object-oriented building extraction method from high resolution image

Li Chaokui, Fang Jun, Chen Jianhui, Zhou Qinglan and Zhou Qian

African Journal of Engineering Research
Published: January 8 2018
Volume 6, Issue 1
Pages 1-6
DOI: https://doi.org/10.30918/AJER.61.17.026

Abstract

Based on high-resolution remote sensing data and the e-Cognition Developer platform, in this paper, we make full use of rich spectral, spatial, texture and geometry information of high resolution QuickBird images in order to classify building areas successfully. The object-oriented multiple-scale segmentation method and the nearest neighborhood and membership function classification method are applied to classify the study area into five land categories; they are residential building, green space, road, leisure area and bare area respectively. On the basis above, the residential building information is extracted eventually. The experiment results show that: compared with the conventional pixel-by-pixel classification method, the object-oriented classification method proposed in this paper can effectively avoid the fragmentation of the segmented regions, which is more complete, accurate and efficient in land classification.

Keywords: Object-oriented, multiple scale segmentation, high resolution, residential buildings extraction.

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