Fractal Feature and Direction Variances(FFDV): A New Algorithm for Urban Area Extraction from Remote Sensing Image

Conventional methods for urban area extraction have several shortcomings: Firstly, there are false detected regions. Secondly, the edge of extracted urban area is not precise. The third is that they can not fit to images with different resolutions. To overcoming these disadvantages, we proposed a new algorithm based on fractal, direction maximal variance(DMAV) and mended direction minimal variance(MDMIV) to extract urban areas exactly. It is divided into three steps: Firstly fractal and MDMIV are used to detect candidate urban regions. Secondly, morphologic and areathreshold are used to eliminate eyelets and get rid of the disturbing small regions. Finally, we use DMAV and MDMIV to find the accurate edge point positions. Lots of experiment results prove that the proposed algorithm is valide and accurate.