Xueying Li, Zongmin Li, Huimin Qiu, G. Hou, Pingping Fan
{"title":"An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples","authors":"Xueying Li, Zongmin Li, Huimin Qiu, G. Hou, Pingping Fan","doi":"10.1080/05704928.2021.1999252","DOIUrl":null,"url":null,"abstract":"Abstract Hyperspectral image (HSI) contains rich spatial and spectral information, which has been widely used in resource exploration, ecological environment monitoring, land cover classification and target recognition. However, the nonlinearity of HSI data and the strong correlation between bands also bring difficulties and challenges to HSI application. In particular, the limited available hyperspectral training samples will lead to the classification accuracy cannot be improved. Therefore, making full use of the advantages of HSI data, through algorithms and strategies to solve the limited training samples, high-dimensional HSI data and effective classification method, so as to improve the classification accuracy. This paper reviews the research results of the feature extraction methods and classification methods of HSI classification in recent years. In addition, this paper expounds five kinds of small sample strategies, and solves the problem of small sample in HSI classification from different angles. Small sample strategy will be the focus of HSI classification research in the future. To solve the problem of small sample classification can greatly promote the application of HSI.","PeriodicalId":8100,"journal":{"name":"Applied Spectroscopy Reviews","volume":"134 ","pages":"367 - 400"},"PeriodicalIF":5.4000,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Spectroscopy Reviews","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1080/05704928.2021.1999252","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 11
Abstract
Abstract Hyperspectral image (HSI) contains rich spatial and spectral information, which has been widely used in resource exploration, ecological environment monitoring, land cover classification and target recognition. However, the nonlinearity of HSI data and the strong correlation between bands also bring difficulties and challenges to HSI application. In particular, the limited available hyperspectral training samples will lead to the classification accuracy cannot be improved. Therefore, making full use of the advantages of HSI data, through algorithms and strategies to solve the limited training samples, high-dimensional HSI data and effective classification method, so as to improve the classification accuracy. This paper reviews the research results of the feature extraction methods and classification methods of HSI classification in recent years. In addition, this paper expounds five kinds of small sample strategies, and solves the problem of small sample in HSI classification from different angles. Small sample strategy will be the focus of HSI classification research in the future. To solve the problem of small sample classification can greatly promote the application of HSI.
期刊介绍:
Applied Spectroscopy Reviews provides the latest information on the principles, methods, and applications of all the diverse branches of spectroscopy, from X-ray, infrared, Raman, atomic absorption, and ESR to microwave, mass, NQR, NMR, and ICP. This international, single-source journal presents discussions that relate physical concepts to chemical applications for chemists, physicists, and other scientists using spectroscopic techniques.