Acta Geodaetica et Cartographica Sinica ›› 2020, Vol. 49 ›› Issue (6): 777-786.doi: 10.11947/j.AGCS.2020.20180423

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

Robust estimation algorithm of active contour model for river extraction in SAR images

HAN Bin, WU Yiquan   

  1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2018-09-11 Revised:2020-02-11 Online:2020-06-20 Published:2020-06-28
  • Supported by:
    The National Natural Science Foundation of China (No. 61573183)

Abstract: To deal with the problem that the existing active contour models are unable to extract rivers in SAR images accurately, this paper presents an new active contour model with L1 norm and Laplacian energies. First, the external energy constraint in form of the L2 norm in the CV model is replaced by the external energy constraint in form of the L1 norm and then the novel energy functional is obtained. Second, an external energy constraint based on the Laplacian kernel function is proposed and added to the above energy functional. Meanwhile, the different adjustment coefficients are assigned to these two external energy constraints. Finally, the mean of median absolute deviations of pixel grayscale values inside and outside the curve is introduced to replace the constant energy weights inside and outside the curve of the model and then the completed proposed model is developed. River extraction is carried out on real SAR images and the results reveal the superiority of the proposed model on both accuracy and efficiency of the river extraction, when compared with the existing active contour models.

Key words: SAR image, river extraction, active contour model, L1 norm, Laplacian kernel function, absolute median deviation

CLC Number: