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Article

Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

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College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
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College of Land Science and Technology, China Agricultural University, Beijing 100083, China
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School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
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Research Center for Ecology and Sustainable Development, Mongolian University of Science and Technology, Ulaanbaatar 14191, Mongolia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1006; https://doi.org/10.3390/rs11091006
Received: 20 March 2019 / Revised: 14 April 2019 / Accepted: 25 April 2019 / Published: 28 April 2019
Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy. View Full-Text
Keywords: convolutional neural networks; land cover classification; data fusion; Sentinel convolutional neural networks; land cover classification; data fusion; Sentinel
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MDPI and ACS Style

Feng, Q.; Yang, J.; Zhu, D.; Liu, J.; Guo, H.; Bayartungalag, B.; Li, B. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sens. 2019, 11, 1006. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091006

AMA Style

Feng Q, Yang J, Zhu D, Liu J, Guo H, Bayartungalag B, Li B. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sensing. 2019; 11(9):1006. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091006

Chicago/Turabian Style

Feng, Quanlong, Jianyu Yang, Dehai Zhu, Jiantao Liu, Hao Guo, Batsaikhan Bayartungalag, and Baoguo Li. 2019. "Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta" Remote Sensing 11, no. 9: 1006. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091006

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