Author:
Yu Jing,Liang Deying,Hang Bo,Gao Hongtao
Abstract
Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing. We first developed a clear–hazy aerial image dataset addressing various types of ground; we then compared the dehazing results of some state-of-the-art methods, including the classic dark channel prior, color attenuation prior, non-local image dehazing, multi-scale convolutional neural networks, DehazeNet, and all-in-one dehazing network. We extended the most suitable method, DehazeNet, to a multi-scale form and added it into a multi-agent deep reinforcement learning network called DRL_Dehaze. DRL_Dehaze was tested on several ground types and in situations with multiple haze scales. The results show that each pixel agent can automatically select the most suitable method in multi-scale haze situations and can produce a good dehazing result. Different ground scenes may best be processed using different steps.
Subject
General Earth and Planetary Sciences
Reference32 articles.
1. A review of atmospheric aerosol research by using polarization remote sensing;Guo;Spectrosc. Spectr. Anal.,2014
2. An overview of atmospheric correction for optical remote sensing satellites;Li;J. Nanjing Univ. Inf. Sci. Technol. Nat. Sci. Ed.,2018
3. Compensation of hyperspectral data for atmospheric effects;Griffin;Linc. Lab. J.,2003
4. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean;Gao;Remote Sens. Environ.,2009
5. Atmospheric correction algorithms for hyperspectral imageries: A review;Minu;Int. Res. J. Earth Sci.,2015
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