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
Highlights
In recent years, superpixel segmentation has been widely used as a preprocessing step of various computer vision applications, such as image segmentation [Farag, Lu, Roth et al (2017)], saliency detection [Liu, Li, Ye et al (2017)], target tracking [Wang, Wang, Liu et al (2018)], image classification [Zhang, Zheng and Xia (2018)], and so on
Α k represent the low frequency components of pixel i and cluster centre Rk ; the parameter α is defined to control the ratio of the frequency spectrum; SID is the acronym of the spectral information divergence, and SAM denotes the spectral angle mapper
In order to evaluate the noise robustness of the superpixel segmentation algorithms, random noise with different intensities is added to the synthetic HS image
Summary
Superpixel segmentation has been widely used as a preprocessing step of various computer vision applications, such as image segmentation [Farag, Lu, Roth et al (2017)], saliency detection [Liu, Li, Ye et al (2017)], target tracking [Wang, Wang, Liu et al (2018)], image classification [Zhang, Zheng and Xia (2018)], and so on. The existing superpixel segmentation algorithms can be categorized as graph-based or gradient-. The current superpixel segmentation algorithms are designed for natural images, where the noise is very limited in these images. When we apply the existing algorithms to segment a HS image into superpixels, the generated superpixels are always affected by the noise. To address this challenge, we propose a novel noise-resistant superpixel segmentation (NRSS) algorithm for the noisy HS images in this paper. In order to make the superpixel algorithm more robust to noise, a new spectral similarity measure is designed in frequency domain. Experimental results show the effectiveness and superiority of the proposed NRSS algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
