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Automatic Auroral Detection in Color All-Sky Camera Images
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 7, Iss 12, Pp 4717-4725 (2014)
- Publication Year :
- 2014
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2014.
-
Abstract
- Every winter, the all-sky cameras (ASCs) in the MIRACLE network take images of the night sky at regular intervals of 10–20 s. This amounts to millions of images that not only need to be pruned, but there is also a need for efficient auroral activity detection techniques. In this paper, we describe a method for performing automated classification of ASC images into three mutually exclusive classes: aurora , no aurora , and cloudy . This not only reduces the amount of data to be processed, but also facilitates in building statistical models linking the magnetic fluctuations and auroral activity helping us to get a step closer to forecasting auroral activity. We experimented with different feature extraction techniques coupled with Support Vector Machines classification. Color variants of Scale Invariant Feature Transform (SIFT) features, specifically Opponent SIFT features, were found to perform better than other feature extraction techniques. With Opponent SIFT features, we were able to build a classification model with a cross-validation accuracy of 91%, which was further improved using temporal information and elimination of outliers which makes it accurate enough for operational data pruning purposes. Since the problem is essentially similar to scene detection, local point description features perform better than global- and texture-based feature descriptors.
- Subjects :
- vision
Atmospheric Science
010504 meteorology & atmospheric sciences
Computer science
Geophysics. Cosmic physics
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-invariant feature transform
02 engineering and technology
01 natural sciences
scene detection
Histogram
0202 electrical engineering, electronic engineering, information engineering
Computer vision
Computers in Earth Sciences
TC1501-1800
0105 earth and related environmental sciences
Feature detection (computer vision)
Aurora
QC801-809
business.industry
Pattern recognition
Statistical model
Ocean engineering
Support vector machine
classification
Feature (computer vision)
020201 artificial intelligence & image processing
Artificial intelligence
business
Pruning (morphology)
Subjects
Details
- ISSN :
- 21511535 and 19391404
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Accession number :
- edsair.doi.dedup.....727adf39986215b2832d9872be08491a