Citation

Chua, Ming Yam and Muhammad Waqar, Mirza and Ji, Yaqi and Sri Sumantyo, Josaphat Tetuko (2019) Unsupervised PolSAR image classification based on sparse representation. Journal International Journal of Remote Sensing. ISSN 0143-1161, eISSN: 1366-5901

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Abstract

A novel unsupervised image classification algorithm which based on the sparse representation theory for polarimetric synthetic aperture radar (PolSAR) image is introduced in this paper. The algorithm conjunctively uses sparse representation-based classification (SRC) theory, dictionary updating method, and label smoothness constraint to update class labels. The unsupervised H/α/A Wishart classification method is introduced to provide the preliminary classification result, from which the initial dictionary and class labels can be extracted. An energy function is defined, and it contains two terms. The first term is based on the sparse representation theory. It reflects the cost of assigning different class labels to a pixel. The second term is label smoothness constraint. It constrains that class labels of neighbouring pixels in flat regions should be the same. By alternately minimizing the energy function, two unknown variables, dictionary and class labels are updated. Optimized class labels are the outputs to compose the final classification result. Extensive experimental results for three PolSAR datasets are analysed to verify the validity of the proposed method. Comparison with other unsupervised/supervised classification methods indicates its superiority.

Item Type:	Article
Uncontrolled Keywords:	Image classification
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	15 Feb 2022 03:03
Last Modified:	15 Feb 2022 03:03
URII:	http://shdl.mmu.edu.my/id/eprint/9145

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