Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 227-230, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-227-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 227-230, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-227-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

STUDY ON THE FEASIBILITY OF RGB SUBSTITUTE CIR FOR AUTOMATIC REMOVAL VEGETATION OCCLUSION BASED ON GROUND CLOSE-RANGE BUILDING IMAGES

C. Li1, F. Li2, Y. Liu3, X. Li4, P. Liu1, and B. Xiao5 C. Li et al.
  • 1College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
  • 2Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 4State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 5Civil Engineering & Architecture School, Hubei University of technology, Wuhan 430068, China

Keywords: 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 (L, 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.