Volume 8 Issue 4
Aug.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(4): 436-447
ZOU Huanxin, LI Meilin, MA Qian, et al. An unsupervised PolSAR image classification algorithm based on tensor product graph diffusion[J]. Journal of Radars, 2019, 8(4): 436–447. doi: 10.12000/JR19057
Citation: ZOU Huanxin, LI Meilin, MA Qian, et al. An unsupervised PolSAR image classification algorithm based on tensor product graph diffusion[J]. Journal of Radars, 2019, 8(4): 436–447. doi: 10.12000/JR19057
shu

An Unsupervised PolSAR Image Classification Algorithm Based on Tensor Product Graph Diffusion

doi: 10.12000/JR19057
  • ①.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

  • ②.

    School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Funds:  The National Natural Science Foundation of China (61331015, 41601436)
More Information
  • Corresponding author: Zou Huanxin, hxzou2008@163.com
  • Received Date: 2019-05-14
  • Rev Recd Date: 2019-07-19
    • Available Online: 2019-07-25
  • Publish Date: 2019-08-01
    Abstract
  • To overcome the difficulty of similarity expression and the effects of speckle noise in unsupervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images, a novel unsupervised PolSAR image terrain classification algorithm based on Tensor Product Graph (TPG) diffusion has been developed herein. Generally, TPG diffusion is usually utilized for optical image segmentation or image retrieval. In the present study, it can be used for PolSAR image terrain classification. TPG diffusion can robustly estimate geodesic distances ; therefore, it can be used for mining the intrinsic affinity between data points. First, the PolSAR image is over-segmented into many superpixels. Second, seven features are extracted based on the segmented superpixels to form a feature vector and construct a similarity matrix by using the Gaussian kernel. Third, TPG diffusion is performed on this similarity matrix to obtain another similarity matrix with stronger discriminability by propagating affinity information along the mainfold structure of data to achieve the global affinity measure. Finally, spectral clustering based on the diffused similarity matrix is adopted to perform terrain classification. Extensive experiments conducted on both simulated and real-world PolSAR images demonstrate that our approach can effectively combine neighborhood information and achieve higher classification accuracy, compared to four other competitive state-of-the-art methods.

     

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