摘要
近年来,基于生成对抗网络的高光谱图像分类方法取得了很大进展.它们虽可以缓解训练样本数量有限的问题,但是容易受到训练数据不平衡的影响,并且存在模式崩溃问题.针对这些问题,提出了一种用于高光谱图像分类的SPCA-AD-WGAN模型.首先,为了解决训练数据不平衡导致分类精度降低的问题,添加了单独的分类器,与判别器分开训练.其次,将Wasserstein距离引入网络,以缓解GAN模型崩溃的问题;在两个HSI数据集上的实验结果表明,SPCA-AD-WGAN具有更好的分类性能.
In recent years,significant progress has been made in the classification of hyperspectral images(HSI)based on generative adversarial nets(GAN).Although they can alleviate the problem of limited training sample size,they are easily affected by imbalanced training data and have the problem of pattern collapse.To this end,a SPCA-AD-WGAN model for HSI classification is proposed.Firstly,to address the issue of reduced classification accuracy caused by imbalanced training data,the study adds a separate classifier and trains it separately from the discriminator.Secondly,it introduces the Wasserstein distance into the network to alleviate the GAN model collapse.The experimental results on two HSI datasets indicate that SPCA-AD-WGAN has better classification performance.
作者
晏远翔
曹国
张友强
YAN Yuan-Xiang;CAO Guo;ZHANG You-Qiang(School of Computer Science and Engineering,Nanjing University of Technology,Nanjing 210094,China;School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《计算机系统应用》
2024年第2期13-22,共10页
Computer Systems & Applications
基金
国家自然科学基金(62201282)
江苏省自然科学基金(BK20231456)。
关键词
高光谱图像
生成对抗网络
分类
hyperspectral image
generative adversarial network(GAN)
classification
