# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 1 Introduction  
Image matching is inevitably challenging when different images of a large scene are being matched. Although local feature points are generally used to match the same location in different images, image matching is difficult in terms of viewpoint, illumination, and temporal changes. There are two types of image-matching method: area-based and feature-based methods [1]. Area-based methods are used to assess the similarity of the pixels between the two images. In feature-based methods, salient features are directly extracted rather than intensity values. In early remote-sensing systems, this work was performed manually. However, this process can now be performed automatically using of local feature point detectors and descriptors [2, 3]. Local feature extraction uses the key points, which are generally based on geometric (scale, location, affine) or photometric (illumination, color) approaches. Moreover, most recent algorithms are robust and invariant to image transformations [4]. Therefore, such techniques are widely used in remote-sensing applications [5], such as object detection [6, 7] and recognition [8, 9], object tracking, scene analysis, classification [10], image matching [11], and retrieval [12, 13]. However, in the case of outdoor scenes, image degradation [14] also occurs because of atmospheric conditions, such as haze [15, 16, 17, 18], smoke, snow [19], fog [20, 21], and rain [22, 23, 24]. Haze is particularly problematic for remote-sensing applications [25] when the images are captured from above the earth's atmosphere. Haze is caused by absorption, scattering, and re-emission in the atmosphere. Therefore, in the case of bad weather, images taken with a camera are degraded by haze depending on the distance. As a result, the degraded images lose their contrast and color fidelity. Conventional methods for removing haze use additional hardware [26, 27, 28, 29, 30, 31], multiple images [32], or additional information [33]. Multiple-image-based dehazing methods remove haze through two or more input images. These methods were developed by [PERSON] [34],in which haze is removed by maximizing the local contrast based on statistics. However, because this approach is not a physical model, it only provides an improvement. [PERSON] and [PERSON] [35] proposed a fast visibility restoration method to preserve edges and corners based on a median filter; however, this method is not suitable for images with a discontinuous depth. [PERSON] et al. [36] recently proposed a simple contrast-based dehazing method without any geometrical information or user interaction. [PERSON] et al. [37] used principal component analysis to enhance haze images by estimating accurate transmission with a random forest. [PERSON] et al. [38] implemented an efficient quad decomposition algorithm to estimate the atmospheric light to solve the color distortion and background noise problems. This method was applied to synthetic and hazy datasets. [PERSON] [39] assumed that the surface shading and transmission functions were locally and statistically uncorrelated to estimate the color of the haze. Moreover, [PERSON] et al. [40] proposed the DCP method, in which it is assumed that dehazed images have local patches that contain a small number of pixels with low intensity in at least one color channel. However, this method produces block artifacts that are patch based. [PERSON] et al. [41] also proposed a guided filter-based dehazing method, but this method only focused on edge-preserving filtering. [PERSON] et al. [42] investigated a new color channel method to remove atmospheric scattering, and obtained channel values and atmospheric light. However, this method focuses on enhancing the quality of the dehazed image using a dual transmission map. [PERSON] et al. [43] proposed a method to solve the underestimation of the transmission of brighter areas using a weighted residual map. [PERSON] et al. [44] also focused on local patches to address structural detail losses using the normalized pixel-wise DCP.