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Remote sensing image dehazing algorithm based on adaptive SLIC

  • role： First author第一作者
  • Affiliation：

    School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China

  • Email：yuhang9551@163.com
  • Introduction：余航，研究方向为合成孔径雷达图像理解与解译、模式识别和计算机视觉。E-mail： yuhang9551@163.com
YU Hang,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China

  • Email：chenyangli@stu.xidian.edu.cn
  • Introduction：李晨阳，研究方向为计算机视觉、图像处理、去雾算法和机器学习。E-mail： chenyangli@stu.xidian.edu.cn
LI Chenyang*,  
  • Affiliation：

    School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China

LIU Zhiheng,  
  • Affiliation：

    School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China

ZHOU Suiping,  
  • Affiliation：

    School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China

GUO Yuru

resumen

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 image is recovered based on the dark channel a priori principle and atmospheric scattering model. The proposed method can achieve adaptive dehazing of remote sensing images without manual parameter setting.Results The proposed algorithm is compared with the four algorithms of He et al., Zhu et al., Han et al., and Nie et al., and the dehazing effects are compared using the publicly available datasets Inria Aerial Image Dataset and RICE Image Dataset. Subjectively, the remote sensing images processed by the proposed algorithm have a more realistic color, more complete dehazing, clearer features, and better retention of image detail information compared with the images processed by the other algorithms. Objectively, the mean value of image information entropy, the peak signal-to-noise ratio, and the structural similarity of the proposed algorithm are 7.56, 22.05, and 0.87, respectively, which are higher than the values for the four other algorithms.Conclusion The proposed dehazing algorithm model integrates the advantages of image enhancement and recovery, thus making the dehazed remote sensing images natural and realistic. It also effectively recovers remote sensing image detail information.

palabra clave

remote sensing image dehazing;Adaptive SLIC;Dark channel a priori;Retinex;superpixel segmentation

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