Landscape Sciences

**163**

**Chapter 8**

*Alfred Cal*

**Abstract**

High-Resolution Object-Based

Building Extraction Using PCA of

Accurate and precise building extraction has become an essential requirement for various applications such as for impact analysis of flooding. This chapter seeks to improve the current and past methods of building extraction by using the principal components analysis (PCA) of LiDAR height (nDSM) and aerial photos (in four RGB and NIR bands) in an object-based image classification (OBIA). This approach uses a combination of aerial photos at 0.1-m spatial resolution and LiDAR nDSM at 1-m spatial resolution for precise and high-resolution building extraction. Because aerial photos provide four bands in the PCA process, this potentially means that the resolution of the image is maintained and therefore building outlines can be extracted at a high resolution of 0.1 m. A total of five experiments was conducted using a combination of different LiDAR derivatives and aerial photos bands in a PCA. The PCA of LiDAR nDSM and RGB and NIR bands combination has proved to produce the best result. The results show a completeness of 87.644%, and a correctness of 93.220% of building extraction. This chapter provides an improvement on the drawbacks of building extraction such as the extraction of small buildings and

**Keywords:** building foot prints, LiDAR, nDSM, principal components analysis,

Over the years, buildings extraction at a high resolution has become an essential requirement for various applications such as flood modeling, urban planning, and 3D building modeling. In flood modeling scenarios, one of the most important structures at risk are buildings. Buildings houses people and other valuable assets, therefore, proper representation of buildings is very important for flood managers. Currently, building extraction methods are being done using a mixture of different data sources and various algorithms. The use of high-resolution aerial imagery and LiDAR are commonly integrated for more accurate building extraction. Aerial image provides spectral information, while LiDAR data provides height and

intensity information. By fusing 2D aerial images and 3D information from LiDAR, complementary information can be exploited to improve automatic building extrac-

There are several techniques used for building extraction in the remote sensing field. One such technique is called image fusion. Image fusion is the combination of

tion processing and the accuracy of the building roof outline [1].

the smoothing with a well-defined building outline.

object-based image classification

**1. Introduction**

LiDAR nDSM and Aerial Photos
