20 September 2017 Application of deep convolutional neural networks for ocean front recognition
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Estanislau Lima, Xin Sun, Yuting Yang, Junyu Dong, "Application of deep convolutional neural networks for ocean front recognition," Journal of Applied Remote Sensing 11(4), 042610 (20 September 2017). https://doi.org/10.1117/1.JRS.11.042610 . Submission: Received: 29 April 2017; Accepted: 22 August 2017
Received: 29 April 2017; Accepted: 22 August 2017; Published: 20 September 2017
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