DichroGAN：从卫星图像中恢复海底空中颜色
DichroGAN: Towards Restoration of in-air Colours of Seafloor from Satellite Imagery
AI总结：
DichroGAN通过条件生成对抗网络从卫星图像中恢复海底的空中颜色，利用两步训练过程和水下图像形成方程实现高效恢复。
摘要：
从卫星图像中恢复海底空中颜色是一项具有挑战性的任务，因为水柱中光的指数衰减。在本研究中，我们提出了DichroGAN，一种专门为此目的设计的条件生成对抗网络（cGAN）。DichroGAN采用两步同时训练：首先，两个生成器利用超光谱图像立方体来估计漫反射和镜面反射，从而获得大气场景辐射。接下来，第三个生成器接收包含每个光谱带特征的生成场景辐射作为输入，而第四个生成器估计水下光传输。这些生成器共同作用以去除光吸收和散射的影响，基于水下图像形成方程恢复海底的空中颜色。DichroGAN在由PRISMA卫星图像衍生出的紧凑数据集上进行训练，该数据集包含RGB图像及其对应的光谱带和掩码。在卫星和水下数据集上的广泛实验表明，DichroGAN在与现有最先进的水下恢复技术相比时表现相当。
英文摘要：
Recovering the in-air colours of seafloor from satellite imagery is a challenging task due to the exponential attenuation of light with depth in the water column. In this study, we present DichroGAN, a conditional generative adversarial network (cGAN) designed for this purpose. DichroGAN employs a two-steps simultaneous training: first, two generators utilise a hyperspectral image cube to estimate diffuse and specular reflections, thereby obtaining atmospheric scene radiance. Next, a third generator receives as input the generated scene radiance containing the features of each spectral band, while a fourth generator estimates the underwater light transmission. These generators work together to remove the effects of light absorption and scattering, restoring the in-air colours of seafloor based on the underwater image formation equation. DichroGAN is trained on a compact dataset derived from PRISMA satellite imagery, comprising RGB images paired with their corresponding spectral bands and masks. Extensive experiments on both satellite and underwater datasets demonstrate that DichroGAN achieves competitive performance compared to state-of-the-art underwater restoration techniques.
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