Shuxian Dong;Wei Feng;Yijun Long;Wenxing Bao;Ke Li;Gabriel Dauphin;Mengdao Xing;Yinghui Quan
{"title":"基于小样本数据扩展和一致性正则化的高光谱图像分类方法","authors":"Shuxian Dong;Wei Feng;Yijun Long;Wenxing Bao;Ke Li;Gabriel Dauphin;Mengdao Xing;Yinghui Quan","doi":"10.1109/LGRS.2024.3494552","DOIUrl":null,"url":null,"abstract":"In the hyperspectral image (HSI) classification, convolutional neural networks (CNNs)-based approaches often struggle with the scarcity of labeled samples. The letter proposes an HSI classification method based on data expansion and consistency regularization with small samples. Specifically, we leverage the pixel-pair feature (PPF) to expand the dataset, which facilitates the adequate tuning of CNN parameters and alleviates the issue of overfitting. In addition, a designed CNN structure is employed to extract discriminative features from the limited number of labeled PPFs and numerous unlabeled PPFs. The CNN is trained via minimizing the weighted sum of supervised and unsupervised losses, where the supervised loss is calculated through the cross-entropy function while the unsupervised loss is evaluated with the consistency regularization item. Moreover, reliable references required in the consistency regularization item are provided after making an exponential moving average (EMA) on the outputs of CNNs at different training epochs. Ultimately, we conduct experiments on three real HSI datasets, and the results show that the proposed approach gains superior classification accuracy compared to several existing CNN-based approaches.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperspectral Image Classification Method Based on Data Expansion and Consistency Regularization With Small Samples\",\"authors\":\"Shuxian Dong;Wei Feng;Yijun Long;Wenxing Bao;Ke Li;Gabriel Dauphin;Mengdao Xing;Yinghui Quan\",\"doi\":\"10.1109/LGRS.2024.3494552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the hyperspectral image (HSI) classification, convolutional neural networks (CNNs)-based approaches often struggle with the scarcity of labeled samples. The letter proposes an HSI classification method based on data expansion and consistency regularization with small samples. Specifically, we leverage the pixel-pair feature (PPF) to expand the dataset, which facilitates the adequate tuning of CNN parameters and alleviates the issue of overfitting. In addition, a designed CNN structure is employed to extract discriminative features from the limited number of labeled PPFs and numerous unlabeled PPFs. The CNN is trained via minimizing the weighted sum of supervised and unsupervised losses, where the supervised loss is calculated through the cross-entropy function while the unsupervised loss is evaluated with the consistency regularization item. Moreover, reliable references required in the consistency regularization item are provided after making an exponential moving average (EMA) on the outputs of CNNs at different training epochs. Ultimately, we conduct experiments on three real HSI datasets, and the results show that the proposed approach gains superior classification accuracy compared to several existing CNN-based approaches.\",\"PeriodicalId\":91017,\"journal\":{\"name\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"volume\":\"22 \",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10747404/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10747404/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hyperspectral Image Classification Method Based on Data Expansion and Consistency Regularization With Small Samples
In the hyperspectral image (HSI) classification, convolutional neural networks (CNNs)-based approaches often struggle with the scarcity of labeled samples. The letter proposes an HSI classification method based on data expansion and consistency regularization with small samples. Specifically, we leverage the pixel-pair feature (PPF) to expand the dataset, which facilitates the adequate tuning of CNN parameters and alleviates the issue of overfitting. In addition, a designed CNN structure is employed to extract discriminative features from the limited number of labeled PPFs and numerous unlabeled PPFs. The CNN is trained via minimizing the weighted sum of supervised and unsupervised losses, where the supervised loss is calculated through the cross-entropy function while the unsupervised loss is evaluated with the consistency regularization item. Moreover, reliable references required in the consistency regularization item are provided after making an exponential moving average (EMA) on the outputs of CNNs at different training epochs. Ultimately, we conduct experiments on three real HSI datasets, and the results show that the proposed approach gains superior classification accuracy compared to several existing CNN-based approaches.