Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

By Pedram Ghamisi, Naoto Yokoya, Jun Yu Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio J. Plaza. IEEE Geoscience and Remote Sensing Magazine, 2017

773 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/32b76ed3a13f3721298866b5333da06305bac040

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Citations in the corpus

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems (2019, methodology): “Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms [20,26,28], with recent advances in airborne and spaceborne technologies providing end users with rich spectral, spatial, and temporal information [1,2].”
Deep learning classifiers for hyperspectral imaging: A review (2019, methodology): “Also, Ghamisi et al. (2017a) provided a complex taxonomy with eight criteria and twenty categories, although none of them are exclusively dedicated to DL methods.”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “It is not easy (and sometimes impossible) to characterize a mixed pixel accurately if it is roughly classiﬁed as a speciﬁc type of substance [83].”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “The endmember selection/extraction, subspace identification, and/or rank selection are all refer to this issue [127]–[129].”
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “2899129 one is spectral dimension, which captures the spectral signature (reflective or emissive properties) of each material in different bands along the electromagnetic spectrum [1].”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, background): “Remote sensing has been substantially influenced by hyperspectral imaging in the past decades [1].”
Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest (2019, methodology): “…a great impact for fostering research in data fusion, but also for development of single-sensor processing, since it is the largest freely available HS dataset, with ten times more labeled data than the widely used Salinas or Pavia datasets [46], or the ﬁrst available multispectral-LiDAR dataset.”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, result): “…amount of available data also involves difﬁculties in comparing different methods that lead to similar results, and this again happens for RS image classiﬁcation studies on benchmark datasets, where near perfect results have been obtained by several, more or less complex, architectures [27,29].”
Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, methodology): “SVM shows its low sensitivity to high dimensionality [13]; therefore, many SVM-based classifiers have been proposed to handle the spectral classification of HSI [14].”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, neutral): “Thus, it is more focused than previous reviews on deep learning [6] while presenting hyperspectral peculiarities from a deep learning point of view, different from [7].”
Deep Few-Shot Learning for Hyperspectral Image Classification (2019, background): “HYPERSPECTRAL image (HSI) analysis can provide end users with abundant spectral and spatial information simultaneously [1].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “(% ) G B FF (% ) [1 36 ] FF c K (% ) [1 39 ] m Lr su b”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “Based on the recent studies published in [6], HSI classification (i.”
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, background): “CD has been one of the most important remote sensing research activities (on methodologies and applications) in recent decades [11].”