Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 467-472, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-467-2012
© 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, 467-472, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-467-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

FEATURE MODELLING OF HIGH RESOLUTION REMOTE SENSING IMAGES CONSIDERING SPATIAL AUTOCORRELATION

Y. X. Chen1, K. Qin1, Y. Liu2, S. Z. Gan1, and Y. Zhan1 Y. X. Chen et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
  • 2Information Technology Simulation Teaching and Research Section, Institute of Chemical Defense of CPLA, Beijing, China

Keywords: high resolution, feature, modelling, spatial autocorrelation, segmentation

Abstract. To deal with the problem of spectral variability in high resolution satellite images, this paper focuses on the analysis and modelling of spatial autocorrelation feature. The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used for the analysis of local spatial autocorrelation within the neighbourhood window determined by the range information of the semivariograms. Two segmentation experiments are conducted via the Fuzzy C-Means (FCM) algorithm which incorporates both spatial autocorrelation features and spectral features, and the experimental results show that spatial autocorrelation features can effectively improve the segmentation quality of high resolution satellite images.