Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex

Abstract

Researchers have extensively applied Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) for segmentation. These networks are neural oscillator networks based on biological frameworks, in which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood, as well as a connection to a global inhibitor. In this paper, we develop a modified LEGION segmentation to extract buildings from high-quality digital surface models (DSMs). The extraction is implemented without assumptions on the underlying structures in the DSM data and without prior knowledge of the number of regions. For complex information hidden in the generated DSM of an urban area, grey level co-occurrence matrix homogeneity is used to measure DSM height texture. We then use this homogeneity to distinguish buildings from trees and identify major oscillator blocks in target buildings, instead of using lateral potential. To segment pixels into different groups, we calculate the weight of the global inhibitor (Wz) from DSM complexity. Building boundaries are traced and regularised after extraction from the segmented DSM. A least squares solution with perpendicular constraints for determining regularised rectilinear building boundaries is proposed, and arc line fitting is performed. This paper presents the concept, algorithms, and procedures of the proposed approach. Experimental results on the Vaihingen region studied in the ISPRS test project are also discussed.