Gao Hongmin, Yao Dan, Wang Mingxia, Li Chenming, Liu Haiyun, Hua Zaijun, Wang Jiawei
College of Computer and Information, Hohai University, Nanjing 211100, China.
Nantong Ocean and Coastal Engineering Research Institute, Hohai University, Nantong 226300, China.
Sensors (Basel). 2019 Jul 25;19(15):3269. doi: 10.3390/s19153269.
Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.
高光谱遥感图像（HSIs）具有很大的研究和应用价值。目前，深度学习已成为研究图像处理的一种重要方法。生成对抗网络（GAN）模型是近年来发展起来的一种典型的深度学习网络，GAN模型也可用于对高光谱遥感图像进行分类。然而，在高光谱遥感图像分类中仍存在一些问题。一方面，由于存在同谱异物现象，若仅根据原始GAN模型从光谱样本中生成样本，会产生错误的细节特征信息。另一方面，原始GAN模型中存在梯度消失问题，且单个判别器的评分能力限制了生成样本的质量。为了解决上述问题，我们引入多判别器协作的评分机制，并在三个高光谱数据集上完成半监督分类。与具有单个判别器的原始GAN模型相比，调整后的准则更加严格和准确，生成的样本能够展现更准确的特征。针对单判别器生成的原始GAN存在的模式崩溃和多样性不足问题，本文提出了一种多判别器生成对抗网络（MDGANs），并研究了判别器数量对分类结果的影响。实验结果表明，引入多判别器提高了模型的判别能力，保证了生成样本的效果，解决了生成光谱样本时的噪声问题，且能提高高光谱遥感图像的分类效果。同时，判别器数量对不同数据集有不同的影响。
