GROUND OBJECT RECOGNITION USING COMBINED HIGH RESOLUTION 
AIRBORNE IMAGES AND DSM 
Qingming Zhan*"*, Yubin Liang", Chi Wei", Yinghui Xiao^" 
“School of Urban Design, Wuhan University, Wuhan 430072, China 
® Research Center for Digital City, Wuhan University, Wuhan 430072, China 
* School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China 
gmzhan@whu.edu.cn; lyb.whu@gmail.com; 544496077 @qq.com; yhxiaoitc@126.com 
Commission III, WG III/4 
KEY WORDS: Ground Object Recognition, High Resolution, LIDAR, DSM, Sparse Representation, L1 Minimization 
ABSTRACT: 
The research is carried on dataset Vaihingen acquired from ISPRS Test Project on Urban Classification and 3D Building 
Reconstruction. Four different types of ground objects are extracted: buildings, trees, vegetation (grass and low bushes) and road. 
Spectral information is used to classify the images and then a refinement process is carried out using DSM. A novel method called 
Sparse Representation is introduced to extract ground objects from airborne images. For each pixel we extract its spectral vector and 
solve Basis Pursuit problem using 11 minimization. The classification of the pixel is same as the column vector of observation matrix 
corresponding to the largest positive component of the solution vector. A refinement procedure based on elevation histogram is 
carried on to improve the coarse classification due to misclassification of trees/vegetation and buildings/road. 
1. INTRODUTION 
In recent years LiDAR (Light Detection And Ranging) has 
emerged as a new technology which provides valuable data in 
various forms and scales for mapping and monitoring land 
cover features. Its use has increased dramatically due to 
availability of high-density LiDAR data as well as high 
spatial/spectral resolution airborne imageries. However the data 
from these different sensors have their own characteristics. 
Spatial information which can be used to derive highly accurate 
DSM of scanned objects can be directly obtained from LiDAR 
data. On the other hand, high resolution airborne imageries 
offer very detailed spectral/textural information of ground 
objects. Although aerial photography has been used as a 
mapping tool for a century, the fusion of aerial photography and 
LiDAR data has only been possible in the past few years due to 
advances in sensor design and data acquisition/processing 
techniques (Baltsavias, 1999). So combining these two kinds of 
complementary datasets is quite promising for improving land 
cover mapping (Tao and Yasuoka, 2002). 
There have been some attempts to fuse LiDAR and high- 
resolution imagery for land cover mapping and very promising 
results are shown in recent years. Haala and Brenner (1999) 
combined a LiDAR derived DSM with three-color-band aerial 
images to apply unsupervised classification based on the 
ISODATA (Iterative Self-Organizing Data Analysis Technique) 
algorithm to normalized Digital Surface Model (nDSM) and 
CIR image. In their experiment, nDSM was used to classify 
objects which had different distribution patterns in elevation 
direction. The low-resolution LiDAR data was greatly 
facilitated to separate trees from buildings by the near-infrared 
band from the aerial imagery. Schenk and Csatho (2002) 
exploited the complementary properties of LiDAR and aerial 
images to extract semantically meaningful information. 
Rottensteiner et al. (2005) used a LiDAR derived DTM and the 
Normalised Difference Vegetation Index (NDVI) from 
multispectal images to detect buildings in densely built-up 
urban areas. The rule-based classification scheme applied 
Dempster-Shafer theory to delineate building regions, 
combining NDVI and the average relative heights to separate 
buildings from other objects. Ali et al. (2005) applied an 
automated object-level technique based on hierarchical decision 
tree to fuse high-resolution imagery and LiDAR data. Sohn and 
Dowman (2007) presented an approach for automatic extraction 
of building footprints in a combination of multispectral imagery 
and airborne laser scanning data. The presented method utilized 
a divide-merge scheme to obtain the recognized building outline. 
A comparison of pixel- and object-level data fusion and 
subsequent classification of LiDAR and high-resolution 
imagery was carried out by Ali et al. (2009). The results showed 
that fusion of the color imagery and the DSM generally 
exhibited better results than sole classification of color imagery. 
The underlying assumption of fusion of multisource data is that 
classification accuracy should be improved due to more 
incorporated features (Tso and Mather, 2001). Image fusion can 
be performed at pixel-, object or feature- and decision-levels 
(Pohl and van-Genderen, 1998; Schistad-Solberg et al., 1994). 
Pixel level fusion focused on the merging of physical 
parameters derived from multisource data. It is very sensitive to 
geo-referencing and pixel spacing and topological information 
is often not used in the fusion and subsequent procedures. 
Object-level image fusion methods usually segment multisource 
data into meaningful objects which consists of many data units. 
* Qingming Zhan, Professor and Associate Dean of School of Urban Design, Deputy Director of Research Centre for Digital City, 
Wuhan University. 
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