NDLI: Semi-supervised convolutional generative adversarial network for hyperspectral image classification

Content Provider	IET Digital Library
Author	Xue, Zhixiang
Abstract	To solve the problem of insufficient annotated samples in hyperspectral image classification, the semi-supervised convolutional generative adversarial network classification model is proposed in this study. The generative adversarial framework constructs an adversarial game, where the generator captures data distribution and generates fake samples, while the discriminator determines whether the input comes from generated or training data. In the proposed method, a deep three-dimensional (3D) convolutional neural network is used to generate the so-called fake cube samples and another 3D deep residual network is designed to discriminate the inputs. Furthermore, the generated samples, labelled and unlabelled samples are put into the discriminator for joint training, and the trained discriminator can determine the authenticity of the sample and the class label. This semi-supervised generative adversarial training strategy can effectively improve the generalisation capability of the deep residual network where the labelled samples are limited. Three widely used hyperspectral images are utilised to evaluate the classification performance of the proposed method: Indian Pines, Pavia University, and Salinas-A. The classification results reveal that the proposed model can improve the classification performance and achieve competitive results compared with the state-of-art methods, especially when there are few training samples.
Starting Page	709
Ending Page	719
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0869
Journal	IET Image Processing
Publisher Date	2019-11-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adversarial Game Classification Performance Computer Vision And Image Processing Technique Deep Residual Network Fake Cube Sample Fake Sample Generative Adversarial Framework Generator Captures Data Distribution Geophysical Image Processing Hyperspectral Image Classification Image Classification Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Insufficient Annotated Sample Knowledge Engineering Technique Labelled Sample Learning in AI Neural Computing Technique Neural Nets Pattern Classification Semisupervised Convolutional Generative Adversarial Network Classification Model Semisupervised Generative Adversarial Training Strategy Statistics Three-dimensional Convolutional Neural Network Trained Discriminator Training Sample Widely Used Hyperspectral Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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