Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 573-577, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-573-2012
© 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, 573-577, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-573-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Aug 2012

02 Aug 2012

GROUND OBJECT RECOGNITION USING COMBINED HIGH RESOLUTION AIRBORNE IMAGES AND DSM

Q. Zhan2,1, Y. Liang3,2, C. Wei3,2, and Y. Xiao2,1 Q. Zhan et al.
  • 1School of Urban Design, Wuhan University, Wuhan 430072, China
  • 2Research Center for Digital City, Wuhan University, Wuhan 430072, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Keywords: 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 l1 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.