Collaborative Computing of Urban Built-Up Area Identification from Remote Sensing Image

Abstract

Urban built-up area is one of the important criterions of urbanization. Remote sensing can quickly acquire dynamic temporal and spatial variation of urban built-up area, but how to identify and extract urban built-up area information from massive remote sensing data has become a bottleneck arousing widespread concerns in the field of the data mining and application for remote sensing. Based on the traditional urban built-up area identification and data mining of remote sensing, this paper proposed a new collaborative computing method for urban built-up area identification from remote sensing image. In the method, the normalized difference built-up index (NDBI) and the normalized differential vegetation index (NDVI) feature images were constructed firstly from the spectrum clustering map; and then the urban built-up area was identified and extracted by the map-spectrum synergy and mathematical morphology methods. Finally, a case of collaborative computing of urban built-up areas in Chongqing city, China is presented. And the experimental results show that the total accuracy of urban built-up area identification in 1988 and 2007 reached 92.58% and 91.41%, the Kappa coefficient reached 0.8933 and 0.8722, respectively, and the good results in the temporal and spatial variation monitoring of urban built-up area are achieved.