Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

By Alberto Signoroni, Mattia Savardi, Annalisa Baronio, Sergio Benini. Journal of Imaging, 2019

352 citations recorded by the source corpus.

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Subfields: Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/2a638d9051e83df405196f4c55af889bdd23a10b

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Citations in the corpus

Multispectral Biometrics System Framework: Application to Presentation Attack Detection (2020, neutral): “Spectral imaging has been studied for over a decade with applications to medical imaging, food engineering, remote sensing, industrial applications and security [8].”
A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, background): “A large number of channels make it difficult if not impossible to apply the existing deep learning frameworks for semantic segmentation (Ball et al., 2017; Signoroni, Savardi, Baronio, & Benini, 2019).”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “In [110], a detailed review of DL with the HSI technique was provided.”
Self-Supervised Learning in Remote Sensing: A review (2022, background): “We refer the interested reader to the numerous surveys on machine learning and deep learning in remote sensing [8, 239, 240, 241, 242, 243, 244] for more details about common downstream tasks and available datasets.”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “In [110], a detailed review of DL with HSI technique was provided.”
A Review on Deep Learning in UAV Remote Sensing (2021, methodology): “Regarding the use of DL models to process highly detailed remotely sensed HSI data, Signoroni et al. [172] summarized usage into classification tasks, object detection, semantic segmentation, and data enhancement, such as denoising, spatial super-resolution, and fusion.”