Experimental research on urban road extraction from high-resolution RS images using Probabilistic Topic Models

Abstract

We introduce a semi-automated algorithm to extract urban road from high-resolution RS image using the Probabilistic Topic Models. First of all, an image collection is generated from a high-resolution image by partitioning it into densely overlapped sub-images. The image collection is divided into two subsets, i.e., training images and testing images. The training images are used to estimate the number of topics, and to learn topic models. The training images are densely overlapped and are folded in using the learned topics to make sure that every pixel in each document is allocated to a topic label. Therefore, every pixel in the initial large image might be allocated multiple topic labels since it might belong to multiple sub-images. By selecting the road segments samples, several cluster centers will be assumed as labels of road objects. The semantic information can improve the extraction accuracy of road segments. The central lines of the road segments will be extracted basing on some image filter algorithms and Hough transform. Experimental results over EROS-B images show that road segments can be effectively detected by the proposed algorithm and an initial road network can be formed