Open Access

ARTICLE

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

Peng Fu^1,2, Qianqian Xu¹, Jieyu Zhang³, Leilei Geng^4,*

1 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
2 School of Information Technologies, The University of Sydney, Sydney, NSW2006, Australia.
3 School of Science, China Pharmaceutical University, Nanjing, 211198, China.
4 Institute of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China.

* Corresponding Author: Leilei Geng. Email: .

Computers, Materials & Continua 2019, 59(2), 509-515. https://doi.org/10.32604/cmc.2019.05250

Abstract

The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noise-resistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information divergence (SID) are combined to enhance the discriminability of the spectral similarity; finally, the superpixels are generated with the proposed frequency-based spectral similarity. Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels. Moreover, the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering (SLIC), where the comparison results prove the superiority of the proposed superpixel segmentation algorithm

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Keywords

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