The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods are facing two problems when performing RS images translation: 1) ignoring the scale discrepancy between two RS datasets which greatly affects the accuracy performance of scale-invariant objects, 2) ignoring the characteristic of real-to-real translation of RS images which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS images translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets, and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDuanGAN. At the end of the paper, a thorough discussion is also conducted to give a reasonable explanation for the improvement of ResiDualGAN. Our source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG.
翻译:在附加注释的数据集上预先训练的遥感图像的语义分解模型的性能会因域差而在另一个未附加注释的数据集上测试时大大降低。Aversarial 基因化方法,例如DualGAN,被用于未受重视的图像到图像的翻译,以尽量减少像素级域差距,这是在不受监督的域适应(UDA)的共同方法之一。然而,现有的图像翻译方法在执行RS图像翻译时面临着两个问题:1) 无视两个RS数据集之间的比例差异,这种差异严重影响到比额表变异物体的精确性能;2 无视RS图像的实时翻译特征,这种图像为模型的培训提供了不稳定的因素。在本文中,ResiDualGAN 的图像翻译建议采用一个网络内再转化模块,用于解决RS数据集的尺度差异,而剩余连接用于加强真实到真实图像翻译的稳定性,改进跨数字-D的跨数字级解释性能性能; REDA-DG的常规性能性能和SEA性能的正常性,也用于我们S-DL的正常性分析方法。