论文标题
自适应下降块增强了高光谱图像分类的生成对抗网络
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
论文作者
论文摘要
近年来，基于生成对抗网络（GAN）的高光谱图像（HSI）分类取得了巨大进步。基于GAN的分类方法可以在某种程度上减轻有限的培训样本困境。但是，一些研究指出，现有的基于GAN的HSI分类方法受到不平衡训练数据问题的严重影响。甘中的歧视者总是与自己矛盾，并试图将假标签与少数级样本相关联，从而损害了分类表现。另一个关键问题是基于GAN的方法中的模式崩溃。该发电机只能在数据空间的狭窄范围内产生样品，这严重阻碍了基于GAN的HSI分类方法的进步。在本文中，我们提出了用于HSI分类的自适应下降块增强生成对抗网络（ADGAN）。首先，为了解决不平衡的培训数据问题，我们将歧视器调整为单个分类器，并且不会自行矛盾。其次，提出了一种自适应下降块（ADAPDROP）作为发电机和鉴别器中用于减轻模式崩溃问题的正规化方法。 ADAPDROP生成具有自适应形状而不是固定尺寸区域的滴面膜，并且减轻了与各种形状处理地面对象的局限性的局限性。三个HSI数据集的实验结果表明，所提出的Adgan比基于GAN的最先进的方法实现了卓越的性能。我们的代码可从https://github.com/summitgao/hc_adgan获得
In recent years, hyperspectral image (HSI) classification based on generative adversarial networks (GAN) has achieved great progress. GAN-based classification methods can mitigate the limited training sample dilemma to some extent. However, several studies have pointed out that existing GAN-based HSI classification methods are heavily affected by the imbalanced training data problem. The discriminator in GAN always contradicts itself and tries to associate fake labels to the minority-class samples, and thus impair the classification performance. Another critical issue is the mode collapse in GAN-based methods. The generator is only capable of producing samples within a narrow scope of the data space, which severely hinders the advancement of GAN-based HSI classification methods. In this paper, we proposed an Adaptive DropBlock-enhanced Generative Adversarial Networks (ADGAN) for HSI classification. First, to solve the imbalanced training data problem, we adjust the discriminator to be a single classifier, and it will not contradict itself. Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue. The AdapDrop generated drop masks with adaptive shapes instead of a fixed size region, and it alleviates the limitations of DropBlock in dealing with ground objects with various shapes. Experimental results on three HSI datasets demonstrated that the proposed ADGAN achieved superior performance over state-of-the-art GAN-based methods. Our codes are available at https://github.com/summitgao/HC_ADGAN
