论文标题
使用深层生成对抗网络现实的河流图像合成
Realistic River Image Synthesis using Deep Generative Adversarial Networks
论文作者
论文摘要
在本文中，我们证明了使用深度学习对逼真的河流形象产生的实际应用。具体而言，我们探索了一种生成的对抗网络（GAN）模型，该模型能够产生高分辨率和现实的河流图像，这些模型可用于支持地表水估计，河流曲折，湿地损失和其他水文研究中的建模和分析。首先，我们创建了一个广泛的架空河流图像存储库，用于培训。其次，我们融合了渐进式生长甘（PGGAN），这是一种迭代训练较小分辨率甘斯以逐渐构建到非常高的分辨率的网络结构，以产生高质量（即1024x1024）合成河图像。借助更简单的gan架构，就训练时间的指数增加和消失/爆炸梯度问题而产生了困难，PGGAN实施似乎会大大减少。这项研究中提出的结果表明，在产生高质量的图像并捕获河流结构和流动的细节以支持水文研究的细节，这通常需要广泛的图像来进行模型性能。
In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1024x1024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological research, which often requires extensive imagery for model performance.
