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Article

Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image

by 1,*, 2, 1 and 1
1
School of Geography and Planning, Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, Sun Yat-sen University (SYSU), Guangzhou 510275, China
2
City College of Dongguan University of Technology, Dongguan 511700, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2455; https://doi.org/10.3390/rs11202455
Received: 23 September 2019 / Revised: 11 October 2019 / Accepted: 18 October 2019 / Published: 22 October 2019
Recent studies have shown that deep learning methods provide useful tools for wetland classification. However, it is difficult to perform species-level classification with limited labeled samples. In this paper, we propose a semi-supervised method for wetland species classification by using a new efficient generative adversarial network (GAN) and Jilin-1 satellite image. The main contributions of this paper are twofold. First, the proposed method, namely ShuffleGAN, requires only a small number of labeled samples. ShuffleGAN is composed of two neural networks (i.e., generator and discriminator), which perform an adversarial game in the training phase and ShuffleNet units are added in both generator and discriminator to obtain speed-accuracy tradeoff. Second, ShuffleGAN can perform species-level wetland classification. In addition to distinguishing the wetland areas from non-wetlands, different tree species located in the wetland are also identified, thus providing a more detailed distribution of the wetland land-covers. Experiments are conducted on the Haizhu Lake wetland data acquired by the Jilin-1 satellite. Compared with existing GAN, the improvement in overall accuracy (OA) of the proposed ShuffleGAN is more than 2%. This work can not only deepen the application of deep learning in wetland classification but also promote the study of fine classification of wetland land-covers. View Full-Text
Keywords: wetland; classification; remote sensing; deep learning; generative adversarial networks wetland; classification; remote sensing; deep learning; generative adversarial networks
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MDPI and ACS Style

He, Z.; He, D.; Mei, X.; Hu, S. Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image. Remote Sens. 2019, 11, 2455. https://doi.org/10.3390/rs11202455

AMA Style

He Z, He D, Mei X, Hu S. Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image. Remote Sensing. 2019; 11(20):2455. https://doi.org/10.3390/rs11202455

Chicago/Turabian Style

He, Zhi, Dan He, Xiangqin Mei, and Saihan Hu. 2019. "Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image" Remote Sensing 11, no. 20: 2455. https://doi.org/10.3390/rs11202455

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