Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17968
Title: Residential area detection from high-resolution remote sensing imagery using corner distribution
Authors: Tao, C
Zou, Z
Ding, X 
Keywords: Corner detection
High-resolution remote sensing imagery
Probability likelihood function
Residential area extraction
Issue Date: 2014
Publisher: 科学出版社
Source: 測繪学报 (Acta geodetica et cartographica sinica), 2014, v. 43, no. 2, p. 164-169+192 How to cite?
Journal: 測繪学报 (Acta geodetica et cartographica sinica) 
Abstract: Traditional residential area detection methods are mainly based on image features, such as texture, spectrum, shape and etc. However, these features are not invariant to scale and illumination changes, which consequently reduce the robust of the existing algorithms. To solve this problem, the proposed method uses local feature for residential area detection from high-resolution remote-sensing imagery, which consists of three steps. Firstly, a large set of local feature points are extracted by Harris corner detector. In order to achieve a reliable extraction of corners from residential areas, two criterions are further proposed to validate and filter them. Afterwards, the extracted corners are incorporated into a likelihood function, and are used to measure the possibility of each pixel belonging to the residential area. Finally, residential areas are extracted by an adaptive binary segmentation method. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.
URI: http://hdl.handle.net/10397/17968
ISSN: 1001-1595
DOI: 10.13485/j.cnki.11-2089.2014.0024
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