Liu Juping, Wang Shiju, Wang Xin, Ju Mingye, Zhang Dengyin
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210000, China.
School of Business, Macquarie University, Sydney 2109, Australia.
Sensors (Basel). 2021 Jun 7;21(11):3926. doi: 10.3390/s21113926.
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.
遥感（RS）是有助于探索更多地表信息的数据采集技术之一。然而，卫星捕获的遥感数据易受成像过程中悬浮颗粒的影响，尤其是对于可见光波段的数据。为弥补这一不足，最近已开展了大量去雾工作和努力，其策略是直接恢复单个模糊数据，而无需使用任何额外信息。在本文中，我们首先将当前可用算法分为三类，即图像增强、物理去雾和数据驱动。然后详细总结了每种算法的优缺点。最后，分别讨论了用于对遥感数据去雾技术的恢复性能进行排名的评估指标以及该技术的应用场景。此外，还阐述了当前可用方法的一些常见不足和未来的研究重点。
