Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

doi:10.3837/tiis.2019.08.006

Cited by 2 publications

(4 citation statements)

References 30 publications

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“…Gao et al, 2021;K. Gao et al, 2021;Kang et al, 2019;C. Wu et al, 2019;Xu et al, 2022;Zhixiang Xue et al, 2022;Zuo et al, 2020) used the same hyperspectral images (HSIs) from a dataset held by the Italian University of Pavia.…”

Section: Variousmentioning

confidence: 99%

“…Some of these authors compared these HSIs with other available HSI datasets: Salinas six papers (Ding et al, 2022;K. Gao et al, 2021;C. Wu et al, 2019;Zhixiang Xue et al, 2022;Zuo et al, 2020), Indian Pinessix papers (Ding et al, 2022;F.…”

Section: Variousmentioning

confidence: 99%

“…Wu et al, 2019;Zhixiang Xue et al, 2022;Zuo et al, 2020), Indian Pinessix papers (Ding et al, 2022;F. Feng et al, 2022;Kang et al, 2019;C. Wu et al, 2019;Xu et al, 2022), Kennedy Space Centertwo papers (H. Xu et al, 2022), and Houstontwo papers (F. Zhixiang Xue et al, 2022).…”

Section: Variousmentioning

confidence: 99%

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Ten deep learning techniques to address small data problems with remote sensing

Safonova¹,

Ghazaryan²,

Stiller³

et al. 2023

Preprint

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Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability, yet this has not been investigated in a structured way. We first introduce ten emerging DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. These techniques have shown promising potential in other scientific disciplines, but have been rarely applied in the RS domain. We also provide guidance on which learning technique to use in various cases, which helps to create a more methodologically robust DL application (and a greater number of them) that can be used to tackle socially important problems with limited data.

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Section: Variousmentioning

confidence: 99%

Section: Variousmentioning

confidence: 99%

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Ten deep learning techniques to address small data problems with remote sensing

Safonova¹,

Ghazaryan²,

Stiller³

et al. 2023

Preprint

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“…Convolutional neural networks use local receptive fields to extract spatial information in images and local sharing mechanisms to reduce network training parameters. Convolutional neural networks are now being applied to HSI classification tasks with greater frequency [13][14][15][16]. In [17], the authors used a CNN in the study of depth representation methods based on spectral features, and the classification effect was better than the traditional SVM algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees

Liu

Wang³

et al. 2020

Entropy

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At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may lead to an overfitting of the model, especially when training data are insufficient. As the performance of the model mainly depends on sufficient data and a large network with reasonably optimized hyperparameters, using DNNs for classification requires better hardware conditions and sufficient training time. This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral features. Furthermore, a multi-layered gradient boosting decision tree model is constructed for classification. We conduct experiments based on three datasets, which in this paper are referred to as the Pavia University, Indiana Pines, and Salinas datasets. It is shown that the proposed FF-DT achieves better performance in classification accuracy, training conditions, and time consumption than other current classical hyperspectral image classification methods.

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Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

Cited by 2 publications

References 30 publications

Ten deep learning techniques to address small data problems with remote sensing

Ten deep learning techniques to address small data problems with remote sensing

A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees

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