Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 643-646, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-643-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 643-646, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-643-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

COMPACT AND HYBRID FEATURE DESCRIPTION FOR BUILDING EXTRACTION

Z. Li1,2, Y. Liu1, Y. Hu3, P. Li3, and Y. Ding3 Z. Li et al.
  • 1Key Laboratory for Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, 400044 Chongqing, China
  • 2Chongqing Academy of Science and Technology, 401123 Chongqing, China
  • 3Chongqing Geomatics Center, 401121 Chongqing, China

Keywords: Building Extraction, Machine Learning, Local Feature, Descriptor, Binary Uniformity Tests, Binary Random Trees, Superpixel segmentation

Abstract. Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.