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Journal of Applied Remote Sensing • new

Application of deep convolutional neural networks for ocean front recognition
Author(s): Estanislau Lima; Xin Sun; Yuting Yang; Junyu Dong
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Paper Abstract

Ocean fronts have been a subject of study for many years, a variety of methods and algorithms have been proposed to address the problem of ocean fronts. However, all these existing ocean front recognition methods are built upon human expertise in defining the front based on subjective thresholds of relevant physical variables. This paper proposes a deep learning approach for ocean front recognition that is able to automatically recognize the front. We first investigated four existing deep architectures, i.e., AlexNet, CaffeNet, GoogLeNet, and VGGNet, for the ocean front recognition task using remote sensing (RS) data. We then propose a deep network with fewer layers compared to existing architecture for the front recognition task. This network has a total of five learnable layers. In addition, we extended the proposed network to recognize and classify the front into strong and weak ones. We evaluated and analyzed the proposed network with two strategies of exploiting the deep model: full-training and fine-tuning. Experiments are conducted on three different RS image datasets, which have different properties. Experimental results show that our model can produce accurate recognition results.

Paper Details

Date Published: 20 September 2017
PDF: 11 pages
J. Appl. Rem. Sens. 11(4) 042610 doi: 10.1117/1.JRS.11.042610
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Estanislau Lima, Ocean Univ. of China (China)
Xin Sun, Ocean Univ. of China (China)
Yuting Yang, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)

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