An Adaptive Haze Removal Method for Single Remotely Sensed Image Considering the Spatial and Spectral Varieties

被引：0
|
作者：
Qi Q. [1 ]
Zhang C. [2 ]
Yuan Q. [3 ]
Li H. [2 ]
Shen H. [2 ]
Cheng Q. [4 ]
机构：
[1] Hubei Institute of Land and Resources, Wuhan
[2] School of Resource and Environmental Sciences, Wuhan University, Wuhan
[3] Guangdong OPPO Mobile Telecommunications Co. Ltd, Shenzhen
[4] School of Urban Design, Wuhan University, Wuhan
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2019年 / 44卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Dark channel prior; Haze removal; Remote sensing image; Spatial adaptive; Spectral adaptive;
D O I：
10.13203/j.whugis20170411
中图分类号：
学科分类号：
摘要：
Remotely sensed images are often degraded due to the haze interference during the imaging process, which greatly reduces their utilization. In order to solve this problem, a spatial-spectral adaptive haze removal method for single remote sensing image is proposed in this paper. Based on the dark prior theory and haze image model, and taking into account the spatial and spectral varieties in the remotely sensed images, our proposed method effectively overcome the difficulties of over-correction on bright terrain and inadequate correction of haze in some wavebands. A bright object index (BOI) is constructed to extract the bright objects with the help of the density segmentation method, and an adaptive correction function is then introduced to refine the misestimated transmittance. Given the influences of atmospheric scattering are wavelength dependent among visible channels, two empirical accommodation coefficients are applied to derive the transmittance of the different channels, achieving the adaptive adjustment of processing intensity in different wavebands. Experimental results show that our proposed method can remove the haze completely and yield visually haze-free images, comparing with the other existing methods. © 2019, Research and Development Office of Wuhan University. All right reserved.
引用
收藏
页码：1369 / 1376
页数：7
相关论文
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