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Remote Sens. 2018, 10(1), 8; doi:10.3390/rs10010008

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

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 Intelligent Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
Meteorological Observation Centre, China Meteorological Administration, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 4 October 2017 / Revised: 19 December 2017 / Accepted: 20 December 2017 / Published: 21 December 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods. View Full-Text
Keywords: ground-based cloud classification; machine learning; transfer of local features; discriminative metric learning ground-based cloud classification; machine learning; transfer of local features; discriminative metric learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, Z.; Li, D.; Liu, S.; Xiao, B.; Cao, X. Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning. Remote Sens. 2018, 10, 8.

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