A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

• 作者: Jieyu Zhang Leilei Geng Peng Fu Qianqian Xu
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Superpixel SEGMENTATION HYPERSPECTRAL IMAGES FOURIER transformation SPECTRAL SIMILARITY random noise
• 年，卷（期）: 2019,（5）
• 页码范围: 509-515
• 页数: 7页
• 分类号: TP3

摘要

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 noiseresistant 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.