Laser & Optoelectronics Progress, Volume. 56, Issue 16, 160101(2019)

Typhoon Classification Model Based on Multi-Scale Convolution Feature Fusion

Peng Lu**, Peiqi Zou, and Guoliang Zou*
Author Affiliations
  • College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
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    In order to enhance the perception for the multi-scale image variation and improve the scale invariance of convolutional neural networks,this study proposes a typhoon classification model based on multi-scale convolutional feature fusion. A multi-scale perception layer is added to convolutional neural networks; then, convolutional features are multi-scale perceived and cascaded. A multi-scale regularization term is then incorporated into the loss function. The residual error of hidden layer weight is minimized and the feature extraction ability is optimized with backpropagation. Finally, multi-scale high-level semantic features are normalized to the probability value of each category using Softmax. The maximum probability value is used as the final classification result of the image. Infrared satellite cloud images are used as the dataset in our experiments to validate the multi-scale perception ability of the model. Experimental results show that the model can effectively perceive and extract the local features of the typhoon cloud map. The generalization ability of the model is verified using two general datasets, i.e., MNIST and CIFAR-10.

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    Peng Lu, Peiqi Zou, Guoliang Zou. Typhoon Classification Model Based on Multi-Scale Convolution Feature Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(16): 160101

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Jan. 28, 2019

    Accepted: Mar. 21, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Lu Peng (plu@shou.edu.cn), Zou Guoliang (glzou@shou.edu.cn)

    DOI:10.3788/LOP56.160101

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