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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 15; pp. 968 - 999
• Main Authors: Ahmad, Muhammad, Shabbir, Sidrah, Roy, Swalpa Kumar, Hong, Danfeng, Wu, Xin, Yao, Jing, Khan, Adil Mehmood, Mazzara, Manuel, Distefano, Salvatore, Chanussot, Jocelyn
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/JSTARS.2021.3133021

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.