Advanced Spectral Classifiers for Hyperspectral Images: A review

By Pedram Ghamisi, Javier Plaza, Yushi Chen, Jun Yu Li, Antonio J. Plaza. IEEE Geoscience and Remote Sensing Magazine, 2017

598 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/24650c6f597ca397fa96ead7ff3891eb2f4800fc

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In Analytical Chemistry

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Citations in the corpus

Capsule Networks for Hyperspectral Image Classification (2019, background): “Many different classification paradigms have been successfully adopted by the remote sensing community in order to build effective hyperspectral imaging (HSI) classifiers [9], [10].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “Thus, considering the aforementioned limitations, HSI analysis is categorized into the following main streams: dimensionality reduction [6]–[8], spectral unmixing [9]–[14], change detection [15]–[17] classification [18]–[21], feature learning for classification [22]–[24], restoration and denoising [”
CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, background): “Hence, it has been applied to pollution monitoring, medical diagnosis, agriculture assessment, and geological exploration [1]–[4].”
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “In addition, there have been several hyperspectral image classification surveys [27]–[29].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[35] P.”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “Compared with traditional shallow modules, it is constructed with multilayer structure, usually deeper than three layers [141].”
Implementation of machine-learning classification in remote sensing: an applied review (2018, background): “Ghamisi et al. (2017) o ﬀ er a recent review of advanced algorithms with a focus on the classi ﬁ cation of hyperspectral data that compare and highlight the strengths and weaknesses of a variety of methods.”
Generative Adversarial Networks for Hyperspectral Image Classification (2018, methodology): “Traditional spectral classification algorithms typically include K nearest neighbors (KNN), maximum likelihood, neural network, and logistic regression [3].”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “For example, Yan etal .in[54]reviewtheusefulnessofhigh-resolutionLiDAR sensor and its application for urban land cover classiﬁcation, or in [7], an algorithmic perspective review for processing of hyperspectral images is provided.”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “For the algorithm setup, see [88] and [93].”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, background): “Detailed information about the aforementioned pixel-wise classifiers can be found in [75,76] where the performances of the classifiers have been critically compared.”
A new deep convolutional neural network for fast hyperspectral image classification (2017, methodology): “, 2016a), so we obtain a 1-D CNN architecture that receives N 1 input vectors, where N is the number of spectral bands (Ghamisi et al., 2017; Hu et al., 2015b; Chen et al., 2016).”
Deep Few-Shot Learning for Hyperspectral Image Classification (2019, methodology): “The most widely used supervised classiﬁers are available in [6].”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “Spectral information is the most important characteristic of HSIs and plays a vital role in the classification tasks [36].”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “Currently, multimedia [156] and computer vision and natural language processing [20] are most promising areas of deep learning application [90].”
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (2021, background): “Because of the richness of spectral information, it has a wide range of applications in various fields, such as land-cover detection, agricultural development, environmental protection, and urban planning [4].”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “This sensor can obtain spectral information from hundreds of narrow spectral bands [104].”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “For instance, hyperspectral images can be used to differentiate between different classes of interest with slightly different spectral characteristics [1].”