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

• Published in: Remote Sensing
• Main Authors: Zhong Zhang, Donghong Li, Shuang Liu, Baihua Xiao, Xiaozhong Cao
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2017
• Subjects: ground-based cloud classification; machine learning; transfer of local features; discriminative metric learning; DML
• Online Access: https://doi.org/10.3390/rs10010008

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.