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Ground-based image analysis: A tutorial on machine-learning techniques and applications
Date Issued
2016-06-07
Date Available
2022-01-06T15:16:36Z
Abstract
Ground-based whole-sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole-sky imagers (WSI) can have high spatial and temporal resolution, which is an important prerequisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, and more. Extracting the valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine-learning techniques have become available to aid with the image analysis. This article provides a detailed explanation of recent developments in these techniques and their applications in ground-based imaging, aiming to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine-learning techniques in ground-based image analysis via three primary applications: segmentation, classification, and denoising.
Type of Material
Journal Article
Publisher
IEEE
Journal
IEEE Geoscience and Remote Sensing Magazine
Volume
4
Issue
2
Start Page
79
End Page
93
Copyright (Published Version)
2016 IEEE
Language
English
Status of Item
Peer reviewed
ISSN
2473-2397
This item is made available under a Creative Commons License
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