Dehazing method for hyperspectral remote sensing imagery with hyperspectral linear unmixing

Yuquan Gan; Bingliang Hu; Desheng Wen; Shuang Wang

doi:10.1117/12.2246656

25 October 2016 Dehazing method for hyperspectral remote sensing imagery with hyperspectral linear unmixing

Yuquan Gan, Bingliang Hu, Desheng Wen, Shuang Wang

Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 1015613 (2016) https://doi.org/10.1117/12.2246656
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China

Abstract

Haze always exists in hyper-spectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyper-spectral images. Specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear. So, how to remove the influence of the haze and improve the applicable efficiency of hyper-spectral images is a popular research point. This paper proposes a dehazing method for hyper-spectral images based on linear unmixing. First, a popular hyper-spectral unmixing method called FUN is used to get the signature of all the end-members and their corresponding abundance. And then, the abundance of the haze end-member is removed and the abundances of the rest end-members are adjusted to satisfy the sum-to-one and non-negative constraint. Lastly, the new abundance and the signature of the end-members are linearly mixed to get the dehazed hyper-spectral images. The experiment result shows that the dehazed hyper-spectral images exhibit better target information and details. The method is effective and available.

Citation Download Citation

Yuquan Gan, Bingliang Hu, Desheng Wen, and Shuang Wang "Dehazing method for hyperspectral remote sensing imagery with hyperspectral linear unmixing", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 1015613 (25 October 2016); https://doi.org/10.1117/12.2246656

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