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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Generative Adversarial Networks for Hyperspectral Image Classification
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Generative Adversarial Networks for Hyperspectral Image Classification

机译：高光谱图像分类的生成对抗网络

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摘要

A generative adversarial network (GAN) usually contains a generative network and a discriminative network in competition with each other. The GAN has shown its capability in a variety of applications. In this paper, the usefulness and effectiveness of GAN for classification of hyperspectral images (HSIs) are explored for the first time. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs and another CNN is used to generate so-called fake inputs. The aforementioned CNNs are trained together: the generative CNN tries to generate fake inputs that are as real as possible, and the discriminative CNN tries to classify the real and fake inputs. This kind of adversarial training improves the generalization capability of the discriminative CNN, which is really important when the training samples are limited. Specifically, we propose two schemes: 1) a well-designed 1D-GAN as a spectral classifier and 2) a robust 3D-GAN as a spectral–spatial classifier. Furthermore, the generated adversarial samples are used with real training samples to fine-tune the discriminative CNN, which improves the final classification performance. The proposed classifiers are carried out on three widely used hyperspectral data sets: Salinas, Indiana Pines, and Kennedy Space Center. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods. In addition, the proposed GANs open new opportunities in the remote sensing community for the challenging task of HSI classification and also reveal the huge potential of GAN-based methods for the analysis of such complex and inherently nonlinear data.
机译：生成对抗网络（GAN）通常包含相互竞争的生成网络和区分网络。 GAN已在各种应用程序中显示出其功能。本文首次探讨了GAN在高光谱图像（HSI）分类中的有用性和有效性。在提出的GAN中，设计了卷积神经网络（CNN）来区分输入，而另一个CNN用于生成所谓的伪输入。前面提到的CNN一起训练：生成CNN尝试生成尽可能真实的伪输入，而区分CNN尝试对真实输入和伪输入进行分类。这种对抗训练可提高判别CNN的泛化能力，这在训练样本有限的情况下非常重要。具体来说，我们提出了两种方案：1）设计完善的1D-GAN作为频谱分类器，以及2）健壮的3D-GAN作为频谱空间分类器。此外，将生成的对抗样本与实际训练样本一起使用，以微调区分性CNN，从而提高了最终的分类性能。拟议的分类器是在三个广泛使用的高光谱数据集上进行的：盐沼，印第安纳州派恩斯和肯尼迪航天中心。获得的结果表明，与最新方法相比，所提出的模型提供了竞争性结果。此外，拟议的GAN为HSI分类的艰巨任务提供了新的机遇，也为遥感领域提供了新的机遇，也揭示了基于GAN的方法在分析此类复杂的和固有的非线性数据方面的巨大潜力。

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