Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 195-203, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 195-203, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2016

21 Jun 2016

SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES

Weihong Cui1,2,3, Guofeng Wang4, Chenyi Feng5, Yiwei Zheng5, Jonathan Li3, and Yi Zhang1 Weihong Cui et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, 129 LuoYu Road, Wuhan, China
  • 2Collaborative Innovation Center for Geospatial Technology, 129 LuoYu Road, Wuhan,China
  • 3Mobile Mapping Lab, University of Waterloo, Canada
  • 4China Highway Engineering Consulting Corporation, China
  • 5Xi’an University of Science and Technology, China

Keywords: Spatial Pyramid Matching, Bag of Visual Words, GrabCut, Segmentation, Target Extraction

Abstract. Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the k-means cluster method, we get the bag of visual words, and then, we adopt three levels’ spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.