Road Network Extraction via Aperiodic Directional Structure Measurement

In this paper, we present a novel aperiodic directional structure measurement (ADSM) toward road network extraction. Based on the observations from Cognitive Psychology regarding the aperiodicity and local directionality, ADSM can well characterize roadlike structures independent of the spectral character and contrast. By exploiting such measurement as guidance, we construct a mask to denote potential road regions. Then, by combining with some common morphology operators, our approach is able to provide robust road centerlines efficiently. We evaluate our approach with data from various satellite sensors and make comprehensive comparisons with previous state-of-the-art methods. Experimental results demonstrate the merit using our ADSM as a metric to identify potential road structures, as well as the effectiveness and efficiency of our road network extraction system.

I. Introduction

Road network extraction is a fundamental but long-standing problem in remote sensing image processing. Robust road extraction systems make it possible to save much labor for transportation data acquisition and can also provide valuable prior knowledge for the detection and recognition of vehicles, buildings, or other objects.