TY  - JOUR
AB  - Purpose 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.Design/methodology/approach 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.Findings 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.Originality/value 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.
VL  - 39
IS  - 1
SN  - 0260-2288
DO  - 10.1108/SR-03-2017-0039
UR  - https://doi.org/10.1108/SR-03-2017-0039
AU  - Huang Shi-Qi
AU  - Wu Wen-Sheng
AU  - Wang Li-Ping
AU  - Duan Xiang-Yang
PY  - 2018
Y1  - 2018/01/01
TI  - Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image
T2  - Sensor Review
PB  - Emerald Publishing Limited
SP  - 17
EP  - 23
Y2  - 2024/04/25
ER  -