Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data

Abstract

The conventional methods of urban impervious surfaces extraction mainly use the shallow-layer machine learning algorithms based on the medium- or low-resolution remote sensing images, and always provide low accuracy and poor automation level because the potential of multi-source remote sensing data are not fully utilized and the low-level features are not effectively organized. In order to address this problem, a novel method (AEIDLMRS) is proposed to automatically extract impervious surfaces based on deep learning and multi-source remote sensing data. First, the multi-source remote sensing data consisting of LIDAR points cloud data, Landsat8 images and Pléiades-1A images are pre-processed, re-sampled and registered, and then the combined features of spectral, elevation and intensity from the multi-source data are denoised using the minimum noise fraction (MNF) method to generate some representative MNF features. A small number of reliable labelled samples are automatically extracted using the fuzzy C-means (FCM) clustering method based on the MNF features. Secondly, the convolutional neural network (CNN) is used to extract the representative features of the neighborhood windows of each pixel in the fused Pléiades-1A image through multi-layer convolution and pooling operations. Finally, the combined features of MNF features and CNN features are pre-learned via the deep belief network (DBN). The DBN parameters are globally optimized jointly using the Extreme Learning Machine (ELM) classifier on the top level and the small set of labelled samples extracted via FCM, and the urban impervious surfaces are distinguished from others based on the trained ELM classifier and morphological operations. Experiments are performed to compare the proposed method with other three related methods in three different experimental regions respectively. Experimental results demonstrate that AEIDLMRS has better accuracy and automation level than the others under relatively good efficiency, and it is more suitable for the extraction of complex urban impervious surfaces.