Comprehensive urban area extraction from multispectral medium spatial resolution remote-sensing imagery based on a novel structural feature

Abstract

ABSTRACTOne of the most important applications of remote sensing is urban area analysis in multispectral images. This article addresses a comprehensive method for urban area extraction in the mentioned images using a combination of classic spectral features and a new structural feature. The spectral features are used for eliminating the non-urban land covers including vegetation, water, shadows, and bright soil. Then the proposed structural feature based on the density of the spectral gradient is utilized for the final separation of urban points (UPs) from non-urban points (NUPs). The proposed method is insensitive to the spectral variation of urban areas caused by variant geographical conditions and coordinates. The proposed approach is applied to a wide variety of geographical areas from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) multispectral data including arid and desert land, mountainous land, and plain area. Furthermore, a reference land-cover map from National Land Cover Data 2001 of USA is used as the ground reference for the accuracy assessment of the proposed method. Results analysis shows better performance of the proposed method in all study sites compared with the other spectral indices and the other methods. In addition, the proposed structural feature has shown promising results compared with the spectral indices when used in its own.