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Open AccessArticle

Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

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Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA 23529, USA
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Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
3
Department of Earth & Atmospheric Sciences, Old Dominion University, Norfolk, VA 23529, USA
4
Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC 20004, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in PRCV 2018.
Remote Sens. 2020, 12(10), 1581; https://doi.org/10.3390/rs12101581
Received: 29 April 2020 / Revised: 12 May 2020 / Accepted: 13 May 2020 / Published: 16 May 2020
Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations. View Full-Text
Keywords: capsule networks; convolutional neural networks; seagrass quantification; transfer learning; deep learning capsule networks; convolutional neural networks; seagrass quantification; transfer learning; deep learning
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Perez, D.; Islam, K.; Hill, V.; Zimmerman, R.; Schaeffer, B.; Shen, Y.; Li, J. Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models. Remote Sens. 2020, 12, 1581.

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