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

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 similari...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Zhang, Zhong, Li, Donghong, Liu, Shuang, Xiao, Baihua, Cao, Xiaozhong
Format: Article in Journal/Newspaper
Language:English
Published: 2018
Subjects:
Ground-based Cloud Classification
Machine Learning
Transfer Of Local Features
Discriminative Metric Learning
Science & Technology
Technology
OBJECT RECOGNITION
FEATURE-EXTRACTION
IMAGES
DISTANCE
PATTERN
CORTEX
Remote Sensing
DML
Online Access:http://ir.ia.ac.cn/handle/173211/21949
https://doi.org/10.3390/rs10010008
Description
Summary: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.

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