International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
     
STUDY ON THE FEASIBILITY OF RGB SUBSTITUTE CIR FOR AUTOMATIC 
REMOVAL VEGETATION OCCLUSION BASED ON GROUND CLOSE-RANGE 
BUILDING IMAGES 
Chang LI *, Fangfang LI, Yawen LIU®, Xi LI“, Pengcheng LIU *, Benlin XIAO* 
? College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China 
leshaka@126.com && lichang@mail.cenu.edu.cn 
b Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 
Changsha 410073, China 
° School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China 
? State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 
Wuhan 430079, China. 
* Civil Engineering & Architecture School, Hubei University of technology, Wuhan 430068, China 
Commission V, V/6 
KEY WORDS: CIR, RGB, Vegetation occlusion, Removal, Segmentation, 3D reconstruction 
ABSTRACT: 
Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings 
are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing 
work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is 
achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference 
Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, 
CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into 
account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras 
don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by 
photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (Z, a, b) feature and support vector machine 
(SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation 
are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave 
the way for texture reconstruction and repair for future 3D reconstruction. 
1. INTRODUCTION 
Photo-realistic 3D models are nowadays required in many 
applications (Ortin and Remondino, 2005). Building 3D- 
reconstruction is an important part of work in Digital City and 
also is a significant part of street landscape, therefore how to 
carry out 3D visualization plays an important role in 3D city 
modelling (LI and Zhou, 2010). Terrestrial images for texture 
mapping streetscapes, which are captured along a narrow street, 
are hardly free of the occlusions which hinder the realistic 
facade texture. Thus, the full automation of texture occlusion 
removal has been regarded as a valuable research for rendering 
street scene (LIU and GUAN, 2010). Occlusions can be moving 
(e.g. pedestrians) or static (e.g. monuments) objects, which 
occlude the full and free visibility of the surface to be textured 
(Ortin and Remondino, 2005). This paper mainly discusses the 
static problem that building is occluded by vegetation. 
In the traditional multispectral remote sensing which is 
achieved by aeronautics and space platforms, the Red and Near- 
infrared (NIR) bands, such as NDVI (Normalized Difference 
Vegetation Index), are useful to distinguish vegetation and 
clouds, amongst other targets. However, especially in the 
ground platform, NIR band is little utilized by compute vision 
  
* Corresponding author. 
and digital photogrammetry which usually only take visible 
light RBG bands into account. Therefore, in the ground close- 
range scene of buildings, whether NIR is necessary to recognize 
vegetation occlusion is discussed and tested. 
2. VEGETATION OCCLUSION SEGMENT AND 
REMOVAL 
2.1 Vegetation segment and removal of CIR image 
Color infrared image (CIR) is not as popular as black and white 
for pictorial use, but it does have its place. Color infrared image 
is sometimes referred to as false color. This is because this 
image is designed to differentiate between colors rather than 
reproduce them accurately. And CIR is very important for data 
acquisition and updating, especially for vegetation (LYON et al., 
1998). 
The Normalized Difference Vegetation Index (NDVI) is a 
simple graphical indicator that can be used to analyze remote 
sensing measurements, typically but not necessarily from a 
space platform, and assess whether the target being observed 
contains live green vegetation or not. NDVI was one of the 
most successful of many attempts to simply and quickly identify