Generative Adversarial Capsule Network With ConvLSTM for Hyperspectral Image Classification

Wang, Wei‐Ye; Li, Heng-Chao; Deng, Yang‐Jun; Shao, Liyang; Lu, Xiaoqiang; Du, Qian

doi:10.1109/lgrs.2020.2976482

Cited by 30 publications

(13 citation statements)

References 13 publications

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“…The work [281] proposed to utilize a 3D CNNbased generator network and a 3D deep residual networkbased discriminator network for HSIC. To learn high-level contextual features combination of both capsule network and convolutional long short-term memory (ConvLSTM) based discriminator model has been proposed in [282] for HSIC.…”

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

222

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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the stateof-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that

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Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

222

View full text Add to dashboard Cite

show abstract

“…Although deep learning methods have made great progress in HSI classification, they still face the problem of limited labeled data. To solve the problem, some scholars have proposed Generative Adversarial Networks (GAN) [32] and other models [33][34][35][36][37][38][39][40] to generate virtual samples to augment the training data. Zhu et al [36] proposed a GANbased [32] classification method for HSI and discussed its practicability and effectiveness in HSI classification.…”

Section: Introductionmentioning

confidence: 99%

“…Progressive growing GAN (PG-GAN) [42] and Wasserstein GAN gradient penalty (WGAN-GP) [43] were also proposed; they gradually increase the depth of the network, making the training process faster. Capsule network (CapsNet) [44] and convolutional long short-term memory (ConvLSTM) [45] were combined in Reference [39]. It designed a new discriminator that extracts low-level features and combines them with local spatial sequence information to form high-level context features.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification

et al. 2021

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Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, which could improve the classification performance. To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). Compared with CVAEGAN, we first use random latent vectors to obtain more enhanced virtual samples, which can improve the generalization performance. Then, we introduce the self-attention mechanism into our model to force the training process to pay more attention to global information, which can achieve better classification accuracy. Moreover, we explore model stability by incorporating the WGAN-GP loss function into our model to reduce the mode collapse probability. Experiments on three data sets and a comparison of the state-of-art methods show that SACVAEGAN has great advantages in accuracy compared with state-of-the-art HSI classification methods.

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“…Zhang et al [27] proposed a 3D multiscale dense network to take full advantage of features at different scales for HSI classification. In addition, the capsule neural network (CapsNet) [28], generative adversarial networks (GANs) [29], and a graph convolutional network (GCN) [30] have also been applied for HSI classification and obtained competitive performance.…”

Section: Introductionmentioning

confidence: 99%

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

2021

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Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.

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Generative Adversarial Capsule Network With ConvLSTM for Hyperspectral Image Classification

Cited by 30 publications

References 13 publications

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

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