基于条件生成对抗网络的低照度遥感图像增强

Low-Illumination Remote Sensing Image Enhancement Based on Conditional Generation Adversarial Network

为了提高低照度遥感图像的可视性,提出一种基于条件生成对抗网络的低照度遥感图像增强方法。首先,为克服样本数据不足,利用正常清晰光照的图像合成低照度图像作为训练样本;然后,将原始低照度遥感图像由RGB色彩空间转换到HSI色彩空间,进行通道拆分,有效分离H、S、I分量,在保持色调分量H不变的前提下,利用条件生成对抗网络和改进的对数变换方法分别处理亮度分量I和饱和度分量S;最后,执行通道合并将处理后的图像从HSI色彩空间转换到RGB色彩空间。在损失函数中引入焦点损失函数,解决样本比例高度不平衡的问题。实验结果表明:所提方法有效地提升了低照度遥感图像的亮度和对比度,为低照度遥感图像增强方法的研究提供了新的思路。

In this study,a method is proposed to enhance the low-illumination remote sensing images based on a conditional generation adversarial network,so as to improve their visibility.First,low-illumination images were synthesized as training samples based on the clear images with normal illumination to solve the problem of insufficient sample data.Then,the original low-illumination remote sensing images were converted from the RGB color space to the HSI color space.Subsequently,channel splitting was performed to effectively separate the H,S,and I components,keeping the hue component H unchanged.Further,the conditional generation adversarial network and the improved logarithmic transformation method were used for processing the luminance component I and the saturation component S,respectively.Finally,channel merging was performed to implement the conversion of processed images from HSI color space to the RGB color space.The phenomenon of highly imbalanced sample proportion can be solved by adding focus loss function to the loss function.The experimental results show that the proposed method effectively improves the brightness and contrast of the low-illumination remote sensing images.Furthermore,this study provides novel concepts with respect to the development of low-illumination remote sensing image enhancement methods.