Volume XL-7/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W4, 155-158, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W4-155-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W4, 155-158, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W4-155-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Jun 2015

26 Jun 2015

Multipolarimetric SAR image change detection based on multiscale feature-level fusion

X. Sun, J. Zhang, and L. Zhai X. Sun et al.
  • Chinese Academy of Surveying and Mapping, 100830 Beijing, China

Keywords: Multipolarimetric SAR, Change Detection, NSCT, Fusion

Abstract. Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision.