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Unsupervised classification of PolSAR image based on tensor product graph diffusion
Author(s): Meilin Li; Huanxin Zou; Qian Ma; Jiachi Sun; Xu Cao; Xianxiang Qin
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Paper Abstract

This paper presents a new unsupervised classification framework based on tensor product graph (TPG) diffusion, which is generally utilized for optical image segmentation or image retrieval and for the first time used for PolSAR image classification in our work. First, the PolSAR image is divided into many superpixels by using a fast superpixel segmentation method. Second, seven features are extracted from the PolSAR image to form a feature vector based on segmented superpixels and construct a similarity matrix by using the Gaussian kernel. Third, TPG diffusion is performed on this similarity matrix to obtain a more discriminative similarity matrix by mining the higher order information between data points. Finally, spectral clustering based on diffused similarity matrix is adopted to automatically achieve the classification results. The experimental results conducted on both a simulated PolSAR image and a real-world PolSAR image demonstrate that our algorithm can effectively combine higher order neighborhood information and achieve higher classification accuracy.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980C (31 July 2019); doi: 10.1117/12.2540397
Show Author Affiliations
Meilin Li, National Univ. of Defense Technology (China)
Huanxin Zou, National Univ. of Defense Technology (China)
Qian Ma, National Univ. of Defense Technology (China)
Jiachi Sun, National Univ. of Defense Technology (China)
Xu Cao, National Univ. of Defense Technology (China)
Xianxiang Qin, Air Force Engineering Univ. (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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