Abstract

Sample generation is an effective way to solve the problem of the insufficiency of training data for hyperspectral image classification. The generative adversarial network (GAN) is one of the popular deep learning methods, which utilizes adversarial training to generate the region of samples based on the required class label. In this paper, we propose cascade conditional generative adversarial nets for hyperspectral image complete spatial-spectral sample generation, named C²GAN. The C²GAN includes two stages. The stage-one model consists of the spatial information generation with a window size that entails feeding random noise and the required class label. The second stage is the spatial-spectral information generation that generates spectral information of all bands in the spatial region by feeding the label regions. The visualization and verification of generated samples based on the Pavia University and Salinas datasets show superior performance, which demonstrates that our method is useful for hyperspectral image classification.

中文翻译：

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级联条件生成对抗性网络，用于空间光谱高光谱样本生成

摘要

样本生成是解决高光谱图像分类训练数据不足的有效途径。生成对抗网络（GAN）是一种流行的深度学习方法，该方法利用对抗训练来根据所需的类别标签生成样本区域。在本文中，我们提出了用于高光谱图像完整空间光谱样本生成的级联条件生成对抗网络，称为C ² GAN。C ²GAN包括两个阶段。第一阶段模型包括空间信息生成，该空间信息生成的窗口大小要求提供随机噪声和所需的类别标签。第二阶段是空间光谱信息生成，其通过馈送标签区域来生成空间区域中所有波段的光谱信息。基于Pavia大学和Salinas数据集生成的样本的可视化和验证显示出卓越的性能，这表明我们的方法可用于高光谱图像分类。