光学遥感影像经常受到云或霾影响，而在多数情况下极少能获取辅助数据用于遥感影像去霾；因此单幅光学遥感影像的图像处理去霾算法成为遥感影像预处理的重要技术。目前，不同研究者设计了多种算法，但是缺乏系统性的总结与对比分析，本文旨在系统性地总结单幅遥感影像去霾算法的研究进展，并提供典型算法的基本原理、优缺点及适用场景。采用文献综合分析方法从霾条件影像成像模型、基础原理和结果评价3方面对当前的去霾算法进行归类总结和原理剖析，然后结合具体应用场景分析算法的适用范围和存在问题，并提出可行的解决方案。常见的去霾算法大体可归纳为暗目标减法、滤波法、暗通道先验法和经验变换法4类，这些算法采用的霾条件影像成像模型包括加法模型、霾传输衰减模型和照明—反射模型等；在算法的评估中，常用的手段有主观分析方法、影像光谱特征分析方法以及图像质量指标评估法等。现有算法并不能适用于所有的场景或图像，存在模型参数难以自适应调整、模型对特殊地物类型敏感、处理结果失真严重等问题；算法的评价主要采用主观对比分析方法，根据应用需求构建客观指标成为目前的热点方向。

Optical remote sensing images are often affected by clouds or haze, and in most cases, auxiliary data are not available for removing haze from the original satellite images. Therefore, the single image-based haze removal method has become the necessary preprocessing technology. A variety of algorithms have been developed by different researchers, but systematic summary and comparative analysis are rare. This paper aims to systematically summarize the research progress of a single image-based haze removal algorithm and provide the basic theory, advantages, and disadvantages and applicable scenarios of typical algorithms. This paper first classifies and summarizes the current haze removal algorithms from three aspects (haze attenuation model, basic theory, and evaluation method), then analyzes the application scope and problems of current algorithms combined with specific application scenarios, and finally presents feasible solutions with respect to special problems. The imaging models used in the haze removal process are the additive, haze degradation, and illumination-reflection models. The additive model, which is simplified from radiation transfer equations, considers that the at-satellite radiance under haze or cloud conditions is the sum of different radiation components, including the constant path radiance, the surface reflected radiance, and spatially varying haze contribution. This model is adopted by the classical dark object subtraction (DOS) method and its various improved versions. The haze degradation model divides the observed light intensity into two components:direct attenuation describing the scene radiance and its decay in the atmosphere and airlight resulting from scattered light. Both components are correlated to the medium transmission that describes the portion of the light that is not scattered and reaches the sensor. Haze removal methods that rely on the dark channel prior (DCP) usually estimate the medium transmission through the haze degradation model. The illumination-reflection model abstracts the observed image as a product of the illumination component of the light source and the reflection component of the object. In the frequency domain, the haze or the cloud signal is mainly concentrated in the low-frequency region and can be suppressed through high-pass filtering. The widely used methods based on the illumination-reflection model include homomorphic filtering and wavelet decomposition. A haze removal procedure generally consists of two consecutive stages:haze detection and haze correction. Haze detection involves obtaining the precise spatial intensity of haze or thin clouds in an image, and haze correction is the process of removing haze influence depending on the estimated haze intensity. The distributions of haze usually vary dramatically in the spatial and temporal domains; as a result, the collection of detailed in situ measurements of haze conditions during the time of image acquisition is almost impossible in practical applications. Thus, single image-based haze removal methods have attracted increasing interest over the past decades. Existing methods retrieved in the literature fall into four common categories:DOS, frequency filtering, DCP, and image transformation-based approaches. DOS-based methods have evolved from the stage where they are suitable for homogeneous haze conditions only to the stage where they are able to compensate for spatially varying haze contaminations. The typical algorithms belong to the dense dark vegetation (DDV) technique and haze thickness map (HTM) method. The DDV technique depends on the empirical correlation between the reflectance of visible bands (usually blue and/or red) and that of a haze-transparent band (e.g., band 7 in the case of Landsat data) for DDV pixels. A DDV-based method would fail to work if a scene does not contain sufficient and evenly distributed vegetated pixels or the correlation of the DDV pixels is significantly different from the standard one. The HTM algorithm estimates haze intensity by searching dark targets within local neighboring blocks instead of searching in an entire scene. The HTM algorithm is feasible for satellite images with high spatial resolutions because pure dark pixels without mixing with bright targets are required in a small local region, but it is unable to handle scenes that have large areas of relatively bright surfaces. Frequency filtering-based approaches operate in the spatial frequency domain assuming that haze contamination is in a relatively low frequency compared with the changeable reflectance of surface covers and can thus be removed by applying a filtering process. Wavelet decomposition and homomorphic filtering are two representative approaches. The major obstacle in applying these methods is determining a cut-off frequency or choosing the wavelet basis. Current solutions rely on empirical criteria and are usually suitable for some special issues. DCP-based methods combine the haze degradation model and DCP, which originates from the statistics of outdoor haze-free images (i.e., in most non-sky patches of haze-free images, at least one color channel has very low intensity at some pixels). When applying DCP-based methods for haze removal in remote sensing images, improvements are required due to the different characteristics between natural scenes and satellite images. Image transformation-based haze removals are initially developed based on the tasseled cap transformation (TCT) because haze contamination seems to be the major contributor to the fourth component of TCT. Haze-optimized transformation (HOT) might be the most widely used transformation-based haze removal method, which supposes that digital numbers of red and blue bands are highly correlated for pixels within the clearest portions of a scene and that this relationship holds for all surface classes. Given that the algorithm relies on only two visible bands, which means that no haze-transparent band is needed, it can be applied to a broad range of satellite images (e.g., Landsat, MODIS, Sentinel-2, QuikBird, and IKONOS). Nevertheless, serious spurious HOT responses exist over non-vegetated areas (e.g., water bodies, snow cover, bare soil, and urban targets), leading to under-correction or overcorrection of these targets. A usual solution is to exclude sensitive land cover types from original HOT and then estimate HOT values for the excluded pixels through spatial inference. Another suggested strategy for addressing this issue is to fill the sinks and flatten the peaks in a HOT image. Other haze removal methods are also involved in band combination, mixed pixel decomposition, or machine learning techniques. For example, multi-scale residual convolutional neural network (MRCNN) is designed for haze removal of Landsat 8 OLI images. MRCNN is able to predict haze intensity by feeding into specific hazy image blocks after it automatically learns the mapping relations between hazy images and their associated haze transmission from sufficient training samples. As for algorithm analysis and evaluation, researchers are inclined to adopt subjective analysis or choose reference images to evaluate spectral consistency before and after haze removal. Recently, image quality indices have been utilized more frequently to evaluate the contrast, brightness, structural consistency, and fidelity of dehazed images. The existing algorithms are not suitable for all scenarios or images, and they face some problems. For instance, parameters are difficult to adjust adaptively, the model is sensitive to special land cover types, and outputted results are seriously distorted. The evaluation of different algorithms is mainly based on subjective comparative analysis, and building objective indicators according to application requirements has become the current research direction.