{"title":"基于特征增强和混合可变形卷积网络的高光谱图像分类方法","authors":"Yunji Zhao, Zhihao Zhang, Wenming Bao, Xiaozhuo Xu, Zhifang Gao","doi":"10.1080/2150704x.2024.2311782","DOIUrl":null,"url":null,"abstract":"In recent years, some hyperspectral image (HSI) classification methods based on deep models have shown excellent performance. Most deep models receive three-dimensional (3D) block structures as inp...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":"12 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hyperspectral image classification method based on feature enhancement and a hybrid deformable convolution network\",\"authors\":\"Yunji Zhao, Zhihao Zhang, Wenming Bao, Xiaozhuo Xu, Zhifang Gao\",\"doi\":\"10.1080/2150704x.2024.2311782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, some hyperspectral image (HSI) classification methods based on deep models have shown excellent performance. Most deep models receive three-dimensional (3D) block structures as inp...\",\"PeriodicalId\":49132,\"journal\":{\"name\":\"Remote Sensing Letters\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-02-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/2150704x.2024.2311782\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/2150704x.2024.2311782","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
A hyperspectral image classification method based on feature enhancement and a hybrid deformable convolution network
In recent years, some hyperspectral image (HSI) classification methods based on deep models have shown excellent performance. Most deep models receive three-dimensional (3D) block structures as inp...
期刊介绍：
Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.
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