Generative Adversarial Capsule Network with ConvLSTM for Hyperspectral Image Classification

Wang，Wei Ye1; Li，Heng Chao1; Deng，Yang Jun1; Shao，Li Yang2; Lu，Xiao Qiang3; Du，Qian4

2021-03-01

10.1109/LGRS.2020.2976482

IEEE Geoscience and Remote Sensing Letters   影响因子和分区

1545-598X

1558-0571

Recently, deep learning has been widely applied in hyperspectral image (HSI) classification since it can extract high-level spatial-spectral features. However, deep learning methods are restricted due to the lack of sufficient annotated samples. To address this problem, this letter proposes a novel generative adversarial network (GAN) for HSI classification that can generate artificial samples for data augmentation to improve the HSI classification performance with few training samples. In the proposed network, a new discriminator is designed by exploiting capsule network (CapsNet) and convolutional long short-term memory (ConvLSTM), which extracts the low-level features and combines them together with local space sequence information to form the high-level contextual features. In addition, a structured sparse L_{2,1} constraint is imposed on sample generation to control the modes of data being generated and achieve more stable training. The experimental results on two real HSI data sets show that the proposed method can obtain better classification performance than the several state-of-the-art deep classification methods.

Capsule network (CapsNet) convolutional neural network (CNN) data augmentation deep learning generative adversarial network (GAN) hyperspectral image (HSI) classification

SCI ; EI

英语

其他

WOS:000622098500031

20211010019298

Classification (of information) ; Deep learning ; Learning systems ; Spectroscopy

Information Theory and Signal Processing:716.1

2-s2.0-85092389255

Scopus

1.Sichuan Provincial Key Laboratory of Information Coding and Transmission,Southwest Jiaotong University,Chengdu,China
2.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
3.Key Laboratory of Spectral Imaging Technology,Chinese Academy of Sciences,Xi'an,China
4.Department of Electrical and Computer Engineering,Mississippi State University,Starkville,United States

Wang，Wei Ye,Li，Heng Chao,Deng，Yang Jun,et al. Generative Adversarial Capsule Network with ConvLSTM for Hyperspectral Image Classification[J]. IEEE Geoscience and Remote Sensing Letters,2021,18(3):523-527.

Wang，Wei Ye,Li，Heng Chao,Deng，Yang Jun,Shao，Li Yang,Lu，Xiao Qiang,&Du，Qian.(2021).Generative Adversarial Capsule Network with ConvLSTM for Hyperspectral Image Classification.IEEE Geoscience and Remote Sensing Letters,18(3),523-527.

Wang，Wei Ye,et al."Generative Adversarial Capsule Network with ConvLSTM for Hyperspectral Image Classification".IEEE Geoscience and Remote Sensing Letters 18.3(2021):523-527.