Abstract
Abstract. LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.
Highlights
Human activity patterns result in increased complexity and spatial extent of built environments(You and Zhang, 2006)
Characteristics extracted from the sparse Light Detection and Range (LiDAR) points and the aerial photos are used as explanatory variables in the geographically weighted regression (GWR) models
3.4.2 Data Conversion: In order to be further integrated with the LiDAR point data, the aerial photo pixel values of the red, blue and green band are assigned to the nearest LiDAR point using the Extraction function in ArcGIS
Summary
Human activity patterns result in increased complexity and spatial extent of built environments(You and Zhang, 2006). High-resolution satellite images and aerial photos are widely adopted in urban studies(Wu and Zhang, 2012). There are some unique characteristics of LiDAR data in an urban environment from which we can glean some important information to be integrated with other types of data for building classification. This study proposes an effective method that integrates the sparse height information of LiDAR data and spectral information from aerial photos (or other remotely sensed images) in geographically weighted regression (GWR) models. The LiDAR data in this paper were collected at 2-meter point spacing in Florida, USA Such point density is insufficient for building extraction. It is found that the hybrid GWR-based method produces better results comparison with those of either the traditional image classification of aerial photos or the classification of LiDAR data alone
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