A method for extracting road characteristics in a high-resolution image

• Main Authors: LIU AIHAN, LUO ZHIMIN, ZHENG XINZHEN, LIN BAOJIA, WANG QIONG, DENG YUQIAO, QIU ZHONGYUAN
• Format: Patent
• Language: Chinese; English
• Published: 16.04.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a method for extracting road characteristics in a high-resolution image. A generator G capable of accurately identifying road characteristics is obtained by optimizing structures of a generator G, a discriminator D and a generative adversarial network V (D, G) and training parameters. In the training process, the difference between the generated sample Pg (x) and the real sample PData (x) is evaluated by using the Waserstein distance, so that the problem of gradient non-convergence of a loss function in the existing KL divergence or JS divergence evaluation can be effectively avoided. The road characteristics obtained through identification of the generator G obtained through the method are closer to the actual situation, and the accuracy is higher. 种高分辨率影像中道路特征的提取方法。其通过对生成器G、鉴别器D和生成对抗网络V(D,G)结构的优化、和参数的训练，获得能够准确识别道路特征的生成器G。训练过程中，本发明通过利用Wasserstein距离来评估生成样本P(x)与真实样本P(x)的差距能够有效避免现有利用的KL散度或JS散度评估时所出现的损失函数的梯度不收敛的问题。由此获得的生成器G，其识别得到的道路特征，其更接近实际情况，准确度更高。