基于自适应SLIC的遥感影像去雾算法  

Remote sensing image dehazing algorithm based on adaptive SLIC

作　　者：余航 李晨阳 刘志恒 周绥平 郭玉茹 YU Hang;LI Chenyang;LIU Zhiheng;ZHOU Suiping;GUO Yuru(School of Aerospace Science and Technology,Xidian University,Xi’an 710126,China)

机构地区：[1]西安电子科技大学空间科学与技术学院,西安710126

出　　处：《遥感学报》2024年第12期3158-3172,共15页NATIONAL REMOTE SENSING BULLETIN

基　　金：中央高校基本科研业务费专项资金(编号:JB211312,XJS221307);陕西省自然科学基础研究计划(编号:2023-JC-QN-0299)。

摘　　要：遥感影像由于雾霾的原因导致清晰度下降,增添了遥感影像目标检测和地物分割任务的困难。本文提出一种基于自适应SLIC的遥感影像去雾算法,首先,针对有雾遥感影像出现局部区域高亮问题,使用改进的Retinex算法,对输入遥感影像进行对比度增强,保留图像细节,以减少伪影现象,扩展图像的对比度动态范围,准确估计遥感影像大气强度值;其次,提出了一种自适应SLIC算法,解决超像素数目参数设定的困难,对输入遥感影像进行超像素分割,避免局部对比度强烈区域对固定窗口的影响,从而获得更加精确的透射率估计;最后,基于暗通道先验原理和大气散射模型恢复出无雾遥感影像。本文使用所提算法和DCP、DOC、EVPM和CHAL的4种算法进行对比,分别在公开数据集Inria Aerial Image Dataset和RICE Image Dataset进行去雾效果比较。主观上,所提算法处理后的遥感影像颜色更真实、去雾更彻底、地物更清晰,能更好的保留影像细节信息;客观上,所提算法处理后的图像信息熵平均值为7.56,峰值信噪比平均值为22.05,结构相似性平均值为0.87,均高于其他4种算法。本文所提出的去雾算法模型,综合了图像增强与恢复的优点,使得去雾后的遥感影像更加自然真实,更好的恢复出遥感影像细节信息。Objective Remote sensing images have degraded clarity because of haze,which makes remote sensing image target detection,feature segmentation,and remote sensing image information interpretation difficult.Remote sensing image defogging based on deep learning is time consuming because of the large number of model parameters and the dependence on the amount of remote sensing image data.Remote sensing image dehazing based on image enhancement does not fully consider the degradation mechanism of remote sensing images in hazy conditions,and as a result,remote sensing images cannot be used for different scenes and easily lose their image information,leading to image distortion.Remote sensing image dehazing based on physical models requires manual parameter setting during transmittance refinement.At the same time,because the contrast of remote sensing images is not completely enhanced,the overall color of dehazed images is dark,and fog remains in local areas.Method In this study,a remote sensing image dehazing method based on image enhancement and physical modeling is proposed to solve the abovementioned problems and improve the quality of remote sensing image dehazing.An adaptive Simple Linear Iterative Clustering(SLIC)-based remote sensing image dehazing algorithm is proposed.First,for the problem of local area highlighting in hazy remote sensing images and the atmospheric intensity value calculation bias problem,an improved Retinex algorithm is used to contrast-enhance the input remote sensing images.The objective is to preserve image details,reduce artifacts,extend the dynamic range of image contrast,and accurately estimate the atmospheric intensity value of remote sensing images.Second,an adaptive SLIC algorithm is proposed to solve the difficulty of setting the number of superpixels and performing superpixel segmentation on the input remote sensing image to avoid the influence of the local contrast intensity region on the fixed window and obtain an accurate transmittance estimation.Last,a haze-free remote sensing im

关 键 词：遥感图像去雾 自适应 SLIC 暗通道先验 RETINEX 超像素分割 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]