Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 3-10, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-3-2017
© 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, 3-10, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-3-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

CHANGE DETECTION OF REMOTE SENSING IMAGES BY DT-CWT AND MRF

S. Ouyang1, K. Fan1,2, H. Wang1, and Z. Wang2 S. Ouyang et al.
  • 1NASG, Satellite Surveying and Mapping Application Center, Bai sheng cun 1 Hao Yuan, Beijing, China
  • 2School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China

Keywords: Change Detection, DT-CWT, MRF, Multi-Scale Decomposition, ICM, Segmentation

Abstract. Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.