ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 499-506, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-499-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
14 Sep 2017
SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES WITH LOCAL ITERATIVE CLUSTERING AND HETEROGENEOUS STATISTICAL MODEL
D. Xiang1, W. Ni1, H. Zhang1, J. Wu1, W. Yan1, and Y. Su2 1Northwest Institute of Nuclear Technology, 710024 Xi’an, China
2College of Electronic Science and Engineering, National University of Defense Technology, 410073 Changsha, China
Keywords: PolSAR, Image Segmentation, Heterogeneous model, Superpixel, Local clustering Abstract. Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel segmentation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed segmentation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.
Conference paper (PDF, 5610 KB)


Citation: Xiang, D., Ni, W., Zhang, H., Wu, J., Yan, W., and Su, Y.: SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES WITH LOCAL ITERATIVE CLUSTERING AND HETEROGENEOUS STATISTICAL MODEL, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 499-506, https://doi.org/10.5194/isprs-annals-IV-2-W4-499-2017, 2017.

BibTeX EndNote Reference Manager XML