**2. Background and related works**

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

the spatial resolution of roof edges [3].

two or more different images to form a new image by using an algorithm to obtain more and better information about an object or a study area [2]. Multispectral data such as aerial photos has spectral and high spatial resolution, meanwhile, LiDAR data has height and intensity information. Thus, buildings can be extracted based on their height from LiDAR and spectral information from aerial photos to improve

There are many image fusion methods that are available, these include intensity hue-saturation (IHS), Brovey transform (BV) and principal component analysis (PCA) [2]. PCA transformation is a technique to reduce the dimensionality of multivariate data whilst preserving as much of the relevant information as possible. It also translates correlated data set to uncorrelated dataset [2]. In this study, a fusion of aerial photos and LiDAR datasets using PCA can be beneficial to accurately detect and extract buildings at a high spatial resolution. The advantage of using PCA as an image fusion technique for feature extraction is that all resulting variables are independent of each other while still retaining the most valuable parts of the input variables. Thus, other type of transformations, such as IHS destroys the spectral characteristics of the image data which is important for feature extraction

For building extraction, some form of image classification technique is needed.

The goal of this chapter is to improve on past and existing methods of building extraction by introducing the use of principal component analysis (PCA) of LiDAR height (nDSM) and aerial photos (RGB and NIR) in an OBIA. This approach was evaluated by comparing the accuracy and quality of building extraction on 5 PCA datasets, this incudes (1) PCA combinations of RGB and nDSM, (2) PCA of RGB, NIR, nDSM and slope, (3) PCA of RGB, nDSM and NDVI, (4) PCA of RGB, NIR,

By evaluating the combinations of bands using the PCA approach for building extraction, the author seeks to answer the research question of this study. To investigate which PCA parameter has the most influence for the detection and extraction of buildings and to determine which band combinations can produce a satisfactory building extraction in terms of their completeness, correctness, and quality. This approach provides the extraction of buildings at a high spatial resolution of 1-m, this allow the building outline to be extracted at the same high spatial resolution. This study introduces a novel approach of using PCA for precise and high-resolution building footprints extraction in an object-based image classification technique in a semi-automated process. Furthermore, this approach was validated by comparing the resultant building footprints using a quantitative and qualitative statistical analysis

and Brovey Transform depress the image values during image fusion [2].

context from a neighborhood surrounding the pixels [5].

nDSM and NDVI, and (5) PCA of RGB, NIR and nDSM.

Pixel-based (spectral pattern recognition), and object-based (spatial pattern recognition) are the two groups of common image classification techniques. Traditionally, image classifications are done with pixel-base using different classifiers in supervised and unsupervised classification (e.g., K-Means, Maximum Likelihood, etc.). These pixel-based procedures analyze the spectral properties of every pixel within the area of interest, without taking into account the spatial or contextual information related to the pixel of interest [4]. Meanwhile, object-based classification techniques start by grouping of neighboring pixels into meaningful areas. Object-based feature extraction is a relatively modern technique for the extraction of objects in urban environments such as buildings and roads, where its advantage lies in the classification of objects represented by a group of pixels. More specifically, image objects are groups of pixels that are similar to one another based on a measure of spectral properties (i.e., color), size, shape, and texture, as well as

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discussed in the results section.

There have been many studies in building extraction techniques with different approaches. Some studies use only LiDAR data, others use only multispectral images and then there are those that use a combination of LiDAR and multispectral images to extract building outlines for various applications. Several methods have been presented for building extraction from LIDAR data during the last decades. Based on the used data, building extraction methods can generally be divided into three categories: 2D (two-dimensional) imagery based, fused 2D-3D information based, and 3D LiDAR based [1].

Studies that use only LiDAR data for building extraction includes [1, 6–9]. A typical step of combining geometry features to extract building is, firstly to filter the DTM from LIDAR data, then derive the DSM data into ground points and non-ground points (including vegetation and building) by height difference [7]. DSM is normally used in flood modeling applications with the combination of DEM to derive a difference image, also called normalize height image. The difference image is a result of subtracting the DEM from the DSM to get the absolute height of buildings and trees in the study area. Digital Surface Models (DSMs) offer the possibility of extracting the elevations of surface features to leave the ground surface DEM [9]. Airborne LiDAR Laser Scanning devices can provide digital surface models that can be used to separate surface features from the ground for modeling flood inundation from rivers in urban and semi-urban environments [9]. However, 3D information provided by LiDAR cannot solve all automated building extraction problems. A typical example is that to separate nearby trees and buildings, extra information, such as color or brightness, is needed to separate features [8]. In the extraction process, only those buildings that are classified as buildings in the point cloud data are extracted, in some cases tiny or small buildings that are not classified in the point cloud data will not be extracted. Another drawback is that this method still has some deficiency to extract out some very small building information. Further improving and updating is still necessary [7]. However, since the method uses LIDAR data alone, the planimetric accuracy is limited by the LIDAR point density. At present, the method does not incorporate smoothing of the boundaries of extracted planar segments [6]. And it is hard to obtain a detailed and geometrically precise boundary using only LIDAR point clouds [10].

Studies that used only images for building extraction includes [3, 11–13]. In remote sensing, building extraction from high resolution imagery has been a common field of research. So far, many algorithms have been presented for the extraction of buildings from satellite images and aerial photos. These algorithms have mainly considered radiometric, geometric, edge detection and shadow criteria approaches [13]. Although promising results have been obtained from these 2D information-based methods, shadows and occlusions leading to significant errors, especially in densely developed areas, cannot be avoided. Consequently, these methods are considered to be insufficiently automated and reliable for practical applications [6].

The third category of building extraction is using a combination of LiDAR data with multispectral images in an image fusion technique. This third approach exploits the mutual benefits of both datasets for accurate building extraction. By fusing 2D images and 3D information from LiDAR, complementary information can be exploited to improve automatic building extraction processing and the accuracy of the building roof outline [1]. This method has been widely studied in [14–16]; Building detection techniques integrating LIDAR data and imagery can be divided into two groups. Firstly, there are techniques that use the LIDAR data as the primary cue for building detection and those which use both the LIDAR data and the imagery as the primary cues to delineate building outlines [15]. In this approach LiDAR height and intensity are usually used along with aerial imagery to improve the classification of buildings. However, the challenges are how to integrate the two data sources for building boundary extraction still arises; few approaches with technical details have thus far been published [14].
