Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the captured spectral information and the correspondin...

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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 15; s. 968 - 999
Hlavní autoři:	Ahmad, Muhammad, Shabbir, Sidrah, Roy, Swalpa Kumar, Hong, Danfeng, Wu, Xin, Yao, Jing, Khan, Adil Mehmood, Mazzara, Manuel, Distefano, Salvatore, Chanussot, Jocelyn
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Classification Computer vision Deep learning Deep learning (DL) Feature extraction feature learning Histograms hyperspectral image classification (HSIC) Hyperspectral imaging hyperspectral imaging (HSI) Image classification Image color analysis Machine learning Polls & surveys Spectra spectral–spatial information Surveying Task analysis Training
ISSN:	1939-1404, 2151-1535
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Abstract	Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data, make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of TML for HSIC and then we will acquaint the superiority of DL to address these problems. This article breaks down the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
AbstractList	Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data, make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of TML for HSIC and then we will acquaint the superiority of DL to address these problems. This article breaks down the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Author	Ahmad, Muhammad Hong, Danfeng Mazzara, Manuel Distefano, Salvatore Yao, Jing Chanussot, Jocelyn Shabbir, Sidrah Wu, Xin Khan, Adil Mehmood Roy, Swalpa Kumar
Author_xml	– sequence: 1 givenname: Muhammad orcidid: 0000-0002-3320-2261 surname: Ahmad fullname: Ahmad, Muhammad email: mahmad00@gmail.com organization: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot, Pakistan – sequence: 2 givenname: Sidrah surname: Shabbir fullname: Shabbir, Sidrah email: sidrah.shabbir@gmail.com organization: Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology (KFUEIT), Rahim yar khan, Pakistan – sequence: 3 givenname: Swalpa Kumar orcidid: 0000-0002-6580-3977 surname: Roy fullname: Roy, Swalpa Kumar email: swalpa@cse.jgec.ac.in organization: Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, West Bengal, India – sequence: 4 givenname: Danfeng orcidid: 0000-0002-3212-9584 surname: Hong fullname: Hong, Danfeng email: hongdf@aircas.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Xin orcidid: 0000-0002-1733-3560 surname: Wu fullname: Wu, Xin email: 040251522wuxin@163.com organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 6 givenname: Jing orcidid: 0000-0003-1301-9758 surname: Yao fullname: Yao, Jing email: jasonyao92@gmail.com organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 7 givenname: Adil Mehmood orcidid: 0000-0003-2220-8518 surname: Khan fullname: Khan, Adil Mehmood email: a.khan@innopolis.ru organization: Institute of Data Science and Artificial Intelligence, Innopolis University, Innopolis, Russia – sequence: 8 givenname: Manuel orcidid: 0000-0002-3860-4948 surname: Mazzara fullname: Mazzara, Manuel email: m.mazzara@innopolis.ru organization: Institute of Software Development and Engineering, Innopolis University, Innopolis, Russia – sequence: 9 givenname: Salvatore surname: Distefano fullname: Distefano, Salvatore email: sdistefano@unime.it organization: Dipartimento di Matematica e Informatica—MIFT, University of Messina, Messina, Italy – sequence: 10 givenname: Jocelyn orcidid: 0000-0003-4817-2875 surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn.chanussot@grenoble-inp.fr organization: CNRS, Grenoble INP, GIPSA-Lab, Universite Grenoble Alpes, Grenoble, France
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Snippet	Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained...
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SubjectTerms	Classification Computer vision Deep learning Deep learning (DL) Feature extraction feature learning Histograms hyperspectral image classification (HSIC) Hyperspectral imaging hyperspectral imaging (HSI) Image classification Image color analysis Machine learning Polls & surveys Spectra spectral–spatial information Surveying Task analysis Training
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Title	Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
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