Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2411-2418, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2411-2418, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2411-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

HIGH-RESOLUTION REMOTE SENSING IMAGE BUILDING EXTRACTION BASED ON MARKOV MODEL

W. Zhao1,2, L. Yan1, Y. Chang3, and L. Gong3 W. Zhao et al.
  • 1Wuhan University School Of Geodesy and Geomatics, 430079, Wuhan, China
  • 2National Geomatics Center of China, 100830, Beijing, China
  • 3The Second Surveying and Mapping Institute of Xinjiang Uygur Autonomous Region, 830002, Xinjiang, China

Keywords: Markov Model, Contourlet Transform, Map Clustering,Spectral Feature Index, Building Extraction, High Resolution Remote Sensing Image

Abstract. With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize “pseudo-buildings” in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.