Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique

Abstract

Stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model to overcome these problems, which can extract BUA features in different scales and recognize the type of land object from multiple scales. The model effectively improves the recognition rate of BUA. The experimental results show that our algorithm can resist the disturbance information, and the classification accuracies are better than support vector machine, backpropagation, random forests, and SDAE. Then we investigate an application in Wuhan (China) metropolitan area analysis with the classification results of our algorithm. The range of the metropolitan area is 1.5-h isochronous circle calculated by Tencent map big data and is divided into three layers: core metropolitan area, subcore metropolitan area, and daily metropolitan. Finally, from the comprehensive statistical data and traffic data, we know that the Wuhan metropolitan area has a “target-shaped” distribution structure radiating outward from the core metropolitan area. It includes five metropolitan development corridors: Wuhan–Huanggang, Wuhan–Xiaogan–Suizhou, Wuhan–Ezhou–Huangshi, Wuhan–Xiantao–Tianmen, and Wuhan–Xianan–Chibi. The corridor is of great significance to the development of metropolitan areas.