Ground-based image analysis: A tutorial on machine-learning techniques and applications

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Title: Ground-based image analysis: A tutorial on machine-learning techniques and applications
Authors: Dev, SoumyabrataWen, BihanLee, Yee HuiWinkler, Stefan
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Date: 7-Jun-2016
Online since: 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
Keywords: Remote SensingImaging science & photographic technologyCloud detectionEnergy minimizationClassificationSparseAlgorithm
DOI: 10.1109/MGRS.2015.2510448
Language: en
Status of Item: Peer reviewed
ISSN: 2473-2397
This item is made available under a Creative Commons License:
Appears in Collections:Computer Science Research Collection

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