Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample size problem. GAN can generate samples by the competition between a generator and a discriminator. However, it is difficult to generate high-quality samples for HSIs with complex spatial–spectral distribution, which may further degrade the performance of the discriminator. To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed. In CA-GAN, the generator and the discriminator not only compete but also collaborate. The shallow to deep features of real multiclass samples in the discriminator assist the sample generation in the generator. In the generator, a joint spatial–spectral hard attention module is devised by defining a dynamic activation function based on a multi-branch convolutional network. It impels the distribution of generated samples to approximate the distribution of real HSIs both in spectral and spatial dimensions, and it discards misleading and confounding information. In the discriminator, a convolutional LSTM layer is merged to extract spatial contextual features and capture long-term spectral dependencies simultaneously. Finally, the classification performance of the discriminator is improved by enforcing competitive and collaborative learning between the discriminator and generator. Experiments on HSI datasets show that CA-GAN obtains satisfactory classification results compared with advanced methods, especially when the number of training samples is limited.

中文翻译：

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基于协作学习和注意机制的高光谱图像生成对抗网络

用有限的样本对高光谱图像（HSI）进行分类是一个具有挑战性的问题。生成对抗网络（GAN）是缓解小样本量问题的有前途的技术。GAN可以通过生成器和鉴别器之间的竞争来生成样本。但是，很难为具有复杂空间光谱分布的HSI生成高质量的样本，这可能会进一步降低鉴别器的性能。为了解决这个问题，提出了一种基于协作学习和注意力机制的对称卷积GAN。在CA-GAN中，生成器和鉴别器不仅竞争而且协作。鉴别器中真实多类样本的浅到深特征有助于生成器中的样本生成。在发电机中 通过定义基于多分支卷积网络的动态激活函数，设计了一个联合的空间光谱硬注意力模块。它促使生成的样本分布在频谱和空间维度上近似于真实HSI的分布，并且它会丢弃误导性和混淆性的信息。在鉴别器中，将卷积LSTM层合并以提取空间上下文特征并同时捕获长期频谱依赖性。最后，通过在鉴别器和生成器之间进行竞争性学习和协作学习，可以提高鉴别器的分类性能。在HSI数据集上进行的实验表明，与高级方法相比，CA-GAN获得了令人满意的分类结果，尤其是在训练样本数量有限的情况下。