{"title":"数据驱动深度学习的高光谱图像分类概述","authors":"Xiaochuan Yu, Mary B. Ozdemir, M. K. Joshie","doi":"10.54097/fcis.v5i3.13999","DOIUrl":null,"url":null,"abstract":"Hyperspectral imaging (HSI) in remote sensing is gaining significant attention due to its complexity, posing challenges for conventional machine learning in achieving accurate classification. The inherent nonlinear relationship between captured spectral information and materials further complicates hyperspectral imaging. Deep learning has emerged as an effective tool for feature extraction, finding widespread applications in image processing tasks. Motivated by its success, this survey integrates deep learning into hyperspectral imaging (HSI) classification, demonstrating commendable performance. The paper systematically reviews existing literature, providing a comparative analysis of strategies. Primary challenges in HSI classification for traditional methods are outlined, emphasizing the advantages of deep learning. Our framework categorizes works into three types: spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks, offering a comprehensive review of recent achievements and diverse approaches. Considering limited training samples in remote sensing and substantial data requirements for deep networks, strategies to enhance classification performance are presented, offering valuable insights for future studies. Experiments apply representative deep learning-based classification methods to real HSIs, providing practical validation. The survey contributes to understanding the current landscape in deep learning-based HSI classification and lays a foundation for future research in this evolving field.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Overview of Hyperspectral Image Classification by Data-driven Deep Learning\",\"authors\":\"Xiaochuan Yu, Mary B. Ozdemir, M. K. Joshie\",\"doi\":\"10.54097/fcis.v5i3.13999\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperspectral imaging (HSI) in remote sensing is gaining significant attention due to its complexity, posing challenges for conventional machine learning in achieving accurate classification. The inherent nonlinear relationship between captured spectral information and materials further complicates hyperspectral imaging. Deep learning has emerged as an effective tool for feature extraction, finding widespread applications in image processing tasks. Motivated by its success, this survey integrates deep learning into hyperspectral imaging (HSI) classification, demonstrating commendable performance. The paper systematically reviews existing literature, providing a comparative analysis of strategies. Primary challenges in HSI classification for traditional methods are outlined, emphasizing the advantages of deep learning. Our framework categorizes works into three types: spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks, offering a comprehensive review of recent achievements and diverse approaches. Considering limited training samples in remote sensing and substantial data requirements for deep networks, strategies to enhance classification performance are presented, offering valuable insights for future studies. Experiments apply representative deep learning-based classification methods to real HSIs, providing practical validation. The survey contributes to understanding the current landscape in deep learning-based HSI classification and lays a foundation for future research in this evolving field.\",\"PeriodicalId\":346823,\"journal\":{\"name\":\"Frontiers in Computing and Intelligent Systems\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Computing and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54097/fcis.v5i3.13999\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54097/fcis.v5i3.13999","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Overview of Hyperspectral Image Classification by Data-driven Deep Learning
Hyperspectral imaging (HSI) in remote sensing is gaining significant attention due to its complexity, posing challenges for conventional machine learning in achieving accurate classification. The inherent nonlinear relationship between captured spectral information and materials further complicates hyperspectral imaging. Deep learning has emerged as an effective tool for feature extraction, finding widespread applications in image processing tasks. Motivated by its success, this survey integrates deep learning into hyperspectral imaging (HSI) classification, demonstrating commendable performance. The paper systematically reviews existing literature, providing a comparative analysis of strategies. Primary challenges in HSI classification for traditional methods are outlined, emphasizing the advantages of deep learning. Our framework categorizes works into three types: spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks, offering a comprehensive review of recent achievements and diverse approaches. Considering limited training samples in remote sensing and substantial data requirements for deep networks, strategies to enhance classification performance are presented, offering valuable insights for future studies. Experiments apply representative deep learning-based classification methods to real HSIs, providing practical validation. The survey contributes to understanding the current landscape in deep learning-based HSI classification and lays a foundation for future research in this evolving field.