Deep learning classifiers for hyperspectral imaging: A review

By Mercedes Eugenia Paoletti, Juan Mario Haut, Javier Plaza, Antonio J. Plaza. 2019

930 citations recorded by the source corpus.

Abstract Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/c8cdd589603347088d61f64a4d845bf11dbb6804

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Citations in the corpus

MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification (2025, background): “Owing to the advantages mentioned above, HSIs are widely applied to diverse applications [6], such as urban mapping, resource exploring, environmental monitoring, and so on.”
Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification (2022, background): “Advances in rapid deep learning technology development have accelerated the development of image processing techniques in a variety of ﬁelds, also contributing to the technical innovation of remote sensing image processing [33].”
Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification (2020, methodology): “We compare classical machine learning and representative deep learning methods available in [63]2: random forest (RF) [64], multinomial logistic regression (MLR) [65], support vector machine (SVM) with radial basis function [1], gated recurrent unit (GRU) [66], long short term memory (LSTM) [67], Re”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “Reference [28] devote their study to the challenges of hyperspectral imaging technologies and review the state-of-the-art of deep learning methodologies used for hyperspectral image classifications.”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “In this layer, several trainable convolution kernels (called also filters) are applied to the previous layer [48].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “However, in HSI analysis, softmax and ReLU are widely employed activation functions [50].”
A Review on Deep Learning in UAV Remote Sensing (2021, background): “In this regard, another paper written by Paoletti et al. [149] organized the source code of DNNs to be easily reproduced.”
A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, background): “Further, it should be noted that different from the previous review papers [12, 29], this paper mainly focuses on the few labeled sample issue, which is considered as the most challenging problem in the HSI classification scenario.”
Image Segmentation Using Deep Learning: A Survey (2020, methodology): “DL-based segmentation methods have been successfully applied to satellite images in remote sensing [189], such as to support urban planning [190] and precision agriculture [191].”