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Appl. Sci. 2018, 8(5), 748; https://doi.org/10.3390/app8050748

Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information

1
Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China
2
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
The Meteorological Observation Centre, China Meteorological Administration, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 4 April 2018 / Revised: 4 May 2018 / Accepted: 5 May 2018 / Published: 9 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Since cloud images captured from different views possess extreme variations, multi-view ground-based cloud recognition is a very challenging task. In this paper, a study of view shift is presented in this field. We focus both on designing proper feature representation and learning distance metrics from sample pairs. Correspondingly, we propose transfer deep local binary patterns (TDLBP) and weighted metric learning (WML). On one hand, to deal with view shift, like variations of illuminations, locations, resolutions and occlusions, we first utilize cloud images to train a convolutional neural network (CNN), and then extract local features from the part summing maps (PSMs) based on feature maps. Finally, we maximize the occurrences of regions for the final feature representation. On the other hand, the number of cloud images in each category varies greatly, leading to the unbalanced similar pairs. Hence, we propose a weighted strategy for metric learning. We validate the proposed method on three cloud datasets (the MOC_e, IAP_e, and CAMS_e) that are collected by different meteorological organizations in China, and the experimental results show the effectiveness of the proposed method. View Full-Text
Keywords: ground-based cloud recognition; transfer deep local binary patterns; weighted metric learning; convolutional neural network ground-based cloud recognition; transfer deep local binary patterns; weighted metric learning; convolutional neural network
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Zhang, Z.; Li, D.; Liu, S.; Xiao, B.; Cao, X. Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information. Appl. Sci. 2018, 8, 748.

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