Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set
Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set
复制标题
小尺寸标记集高光谱图像分类的对抗性表示学习
DOI：
10.3390/rs14112612
复制
发表时间：
2022
期刊：
影响因子：
5
通讯作者：
Li Wang
中科院分区：
文献类型：
--
作者：
Shuhan Zhang;Xiaohua Zhang;Tianrui Li;Hongyun Meng;Xianghai Cao;Li Wang
高光谱图像分类是高光谱技术的主要研究内容之一。现有的基于深度学习的HSI分类算法使用大量的标记样本来训练模型，以确保良好的分类效果，但当标记样本不足时，深度学习模型容易出现过拟合。在实际应用中，有大量的未标记样本没有得到有效利用，因此研究半监督方法具有重要意义。针对高光谱图像分类中的小样本问题，提出了一种基于产生式对抗性网络(ARL-GAN)的对抗性表示学习方法，该方法以半监督方式将GAN应用于表示学习领域。提出的方法具有以下显著优点。首先，我们构建了一个高光谱图像块生成器，它的输入是从编码器提取的特征向量，并使用编码器作为特征提取器来提取更多的鉴别信息。其次，用鉴别器输出的分类概率的距离代替均方根误差来衡量生成的图像块与实际图像之间的误差，从而使编码者能够提取更多有用的信息用于分类。第三，在高光谱图像分类中，利用遗传算法和条件熵提高了未标记数据的利用率，解决了小样本问题。在三个公开数据集上的实验表明，与其他方法相比，该方法在标记样本数量较少的情况下获得了更好的分类精度。
Hyperspectral image (HSI) classification is one of the main research contents of hyperspectral technology. Existing HSI classification algorithms that are based on deep learning use a large number of labeled samples to train models to ensure excellent classification effects, but when the labeled samples are insufficient, the deep learning model is prone to overfitting. In practice, there are a large number of unlabeled samples that have not been effectively utilized, so it is meaningful to study a semi-supervised method. In this paper, an adversarial representation learning that is based on a generative adversarial networks (ARL-GAN) method is proposed to solve the small samples problem in hyperspectral image classification by applying GAN to the representation learning domain in a semi-supervised manner. The proposed method has the following distinctive advantages. First, we build a hyperspectral image block generator whose input is the feature vector that is extracted from the encoder and use the encoder as a feature extractor to extract more discriminant information. Second, the distance of the class probability output by the discriminator is used to measure the error between the generated image block and the real image instead of the root mean square error (MSE), so that the encoder can extract more useful information for classification. Third, GAN and conditional entropy are used to improve the utilization of unlabeled data and solve the small sample problem in hyperspectral image classification. Experiments on three public datasets show that the method achieved better classification accuracy with a small number of labeled samples compared to other state-of-the-art methods.