Recent remote sensing literature has seen generative adversarial network (GAN)-based models developed for hyperspectral image classification, especially in a spatiospectral manner. The intuition is that training classifiers with additional generated hyperspectral data improves model generalization and hence increases classification accuracy. Existing GAN-based hyperspectral image classification methods tend to straightforwardly characterize spatiospectral characteristics of hyperspectral data subject to basic distributions (e.g., Gaussian or uniform distributions). However, hyperspectral imageries have high dimensions in both spectral and spatial representations, which are possibly derived from a latent space following more sophisticated distributions. In this scenario, we believe that comprehensively modeling the latent space would favor accurate hyperspectral classification. To this end, we develop a graphical adversarial learning (GAL) framework that explores the latent variable structure for generating diversified hyperspectral samples. The comprehensive modeling strategy enables GAL to be capable of accurately characterizing full-depth hyperspectral data such that it establishes an end-to-end framework that does not require reduction on spectral bands. We conduct extensive experiments on three public hyperspectral datasets in terms of processing full-depth hyperspectral images without dimension reduction. The experimental results validate that, first, our GAL excels at full-depth hyperspectral image classification, and second, our GAL is competitive with state-of-the-art methods which use global data (i.e., both training and testing data) for learning spectral reduction.

中文翻译：

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通过图形对抗学习深入研究高光谱图像分类


最近的遥感文献已经看到了基于生成对抗网络（GAN）的模型，用于高光谱图像分类，特别是以空间光谱方式。直觉是，使用额外生成的高光谱数据训练分类器可以提高模型泛化能力，从而提高分类准确性。现有的基于 GAN 的高光谱图像分类方法倾向于直接表征受基本分布（例如高斯分布或均匀分布）影响的高光谱数据的空间光谱特征。然而，高光谱图像在光谱和空间表示方面都具有高维度，这可能源自遵循更复杂分布的潜在空间。在这种情况下，我们认为对潜在空间进行全面建模将有利于准确的高光谱分类。为此，我们开发了一个图形对抗学习（GAL）框架，该框架探索用于生成多样化高光谱样本的潜在变量结构。综合建模策略使 GAL 能够准确表征全深度高光谱数据，从而建立不需要减少光谱带的端到端框架。我们在三个公共高光谱数据集上进行了广泛的实验，在不降维的情况下处理全深度高光谱图像。实验结果验证了，首先，我们的 GAL 擅长全深度高光谱图像分类，其次，我们的 GAL 与使用全局数据（即训练和测试数据）进行学习的最先进方法具有竞争力光谱减少。