This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images.
To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm.
The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm.
Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.
This work was supported by Natural Science Foundation of China (No. 41574008), Scientific Research Program Funded by Shaanxi Provincial Education Department (No.16JK2234) and Special Foundation for Special Talents of Xijing University (No. XJ17T04). The authors thank the Space Application Engineering and Technology Center of Chinese Academy for providing Tiangong-1 hyperspectral remote sensing image data.
Huang, S., Wu, W., Wang, L. and Duan, X. (2019), "Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image", Sensor Review, Vol. 39 No. 1, pp. 17-23. https://doi.org/10.1108/SR-03-2017-0039Download as .RIS
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