CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks

Neagoe, Victor-Emil; Diaconescu, Paul

doi:10.1109/comm48946.2020.9142021

Cited by 11 publications

(6 citation statements)

References 17 publications

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“… Addressing and resolving multiple issues regarding the handling and analyzing the HSI data, at a time, depending upon the methods that are chosen for mixing/hybridizing [ 179 – 183 ]. Coherence in time, space, and cost complexities [ 184 – 186 ]. Better interpretability, quality, effectivity leading to the construction of a more refined framework [ 180 , 182 , 183 , 187 – 194 ].…”

Section: Discussionmentioning

confidence: 99%

“…Some other hybridizations are also explored by researchers, such as SRC with mathematical index of divergence-correlation [ 192 ], Gabor-cube filter [ 193 ], and ELM [ 83 , 85 ]; ELM with CNN [ 86 ] and TL [ 26 ]; AL based on super-pixel profile [ 201 , 202 ], AL with CNN [ 203 ], CapsNet [ 204 ], CNN [ 204 , 205 ], and TL [ 151 , 184 ]; CNN with attention-aided methodology [ 172 , 173 , 185 ] and GAN [ 186 ]; GAN with dynamic neighborhood majority voting mechanism [ 194 , 197 ], CapsNet [ 175 , 176 , 206 , 207 ]; and TL with MRF [ 70 ]. These articles depict the highly tenacious performance with literal mitigation of the computational complexities enforced on the raw HSI data to build a strong and enhanced model for achieving higher accuracy than ever.…”

Section: Discussionmentioning

confidence: 99%

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Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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“…Data augmentation helps diversify training data without new labeling costs, thus leading to more robust classification and adequate classification. In remotely sensed-based classification, training data have been flipped and rotated [225,226], mirrored across horizontal, vertical, and diagonal axes on HS [226,227] and lidar data [228], mixup strategy [229], and generation of virtual training samples through Generative Adversarial Networks (GANs) [230] on HS data. In addition, noise is proven to be suited as a data augmentation type.…”

Section: Classification Of Urban Land Cover Classesmentioning

confidence: 99%

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

et al. 2021

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Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

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“…With the development of deep learning, many networks with superior performance have been used in HSI classification, such as convolutional neural networks(CNNs) 5,6 ,Stacked Auto-encoder (SAE) 7 , Recurrent Neural Network(RNN) 8 , generative adversarial networks(GAN) 9 and deep belief networks (DBN).Among those methods, graph convolutional network(GCN) 11 has unique advantages in processing HSI. As a special kind of Laplacian smoothing, graph convolution operation can aggregate feature information from neighborhood node for every node.…”

Section: Introductionmentioning

confidence: 99%

Mutual learning graph convolutional network for hyperspectral image classification

Jia

Zhan

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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Graph convolutional networks (GCNs) have achieved great success in hyperspectral image (HSI) classification. However, there are also some difficulties that need to be solved, such as the lack of enough labeled samples in the training process and the difference between the numbers of different land-cover analogies is too large, which will lead to poor classification performance. In order to alleviate those problems, we propose a Mutual Learning Graph Convolutional Network (called MLGCN) with an imbalance loss, which trains two GCN simultaneously, and these two GCNs can learn from each other by using mutual learning strategy in the training process. Experiments on three real data sets show that the proposed MLGCN method achieves state-of-the-art results in terms of three classification evaluation metrics, including overall accuracy (OA), average accuracy (AA), kappa coefficient (κ),.

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CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks

Cited by 11 publications

References 17 publications

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Mutual learning graph convolutional network for hyperspectral image classification

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