Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule net...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 18; číslo 9; s. 3153
Hlavní autoři: Deng, Fei, Pu, Shengliang, Chen, Xuehong, Shi, Yusheng, Yuan, Ting, Pu, Shengyan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 18.09.2018
MDPI
Témata:
capsule network
Classification
deep learning
hyperspectral
image classification
Laboratories
Neural networks
possibility density
Probability
Remote sensing
World Wide Web
Beijing China
United States > US
China
capsule network
deep learning
image classification
possibility density
hyperspectral
ISSN:1424-8220, 1424-8220
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Shrnutí:Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).
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ISSN:1424-8220
1424-8220
DOI:10.3390/s18093153