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Noise robust band selection method for hyperspectral images

  • role： First author第一作者
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：luyan@sdust.edu.cn
  • Introduction：路燕，研究方向为智能信息处理、遥感图像分类和目标检测研究。E-mail： luyan@sdust.edu.cn
LU Yan,  
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

REN Yue,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：cuibinge@sdust.edu.cn
  • Introduction：崔宾阁，研究方向为高光谱遥感、海洋和海岸带遥感监测技术研究。E-mail： cuibinge@sdust.edu.cn
CUI Binge*

Resümee

Most proposed hyperspectral image band selection methods only consider the problem of band information redundancy and ignore the noise level of the selected bands. Accordingly, the representative band subset may contain high-noise bands, which is not conducive to subsequent semantic segmentation, image classification, and other applications. In response to this problem, this work proposes a noise-robust band selection method based on Pearson correlation coefficient, Information Entropy and Noise Level, referred to as PIENL.In the proposed PIENL method, the Pearson correlation coefficient is first used to calculate the correlation between the bands, and the band correlation matrix is constructed. Then, the spectral bands of the hyperspectral image are divided into several subspaces of the same size, and an optimal subspace division objective function adapted to the Pearson correlation coefficient is constructed to adjust the division points of the subspace. Finally, a new band information measurement criterion is proposed, which observes the band information entropy and noise level at the same time and uses the noise level as a penalty item in the objective function of the optimization problem. According to this criterion, the spectral band with high information entropy and low noise level in each subspace is selected as the representative band.Experiments were conducted on three public hyperspectral datasets of Indian Pines, Salinas, and Washington DC. Different band selection methods are evaluated using the average correlation degree of bands, classification accuracy, and the noise robustness. The experimental results show that this proposed PIENL method demonstrated outstanding band selection performance in terms of class separability, average correlation of representative bands, and noise robustness compared with the other advanced band selection methods.The PIENL method has strong robustness to noise and has achieved significant results on hyperspectral datasets containing noise bands. We can conclude that: (1) The similarity measurement method based on the Pearson correlation coefficient is more suitable for measuring the spectral difference between the noisy hyperspectral image bands compared with Euclidean distance; (2) Considering both information entropy and noise level to measure band information is helpful to select representative bands of hyperspectral image; (3) The representative bands selected by PIENL have better class separability. Compared with other advanced band selection methods, the overall accuracy of PIENL method is improved by 3%—13%, 1.5%—6.0% and 1%—6% respectively on the three datasets with high-noise bands removed. The overall accuracy is improved by 6%—11%, 2%—8% and 3%—7% respectively on the three datasets containing high-noise bands. This also shows that PIENL has better performance on hyperspectral images that contain high-noise bands.

Schlüsselwort

hyperspectral;band selection;noise robustness;subspace partition;search criteria

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