Journal article
Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images
Analyst (London), Vol.147(22), pp.5018-5027
09/26/2022
DOI: 10.1039/d2an01065h
Abstract
Pattern recognition methodology was developed for the automated detection of marine oil spills in passive infrared multispectral remote sensing images. The images employed in this work were collected from the Deepwater Horizon oil spill accident in 2010. The imaging instrument for data collection was a downward-looking infrared line scanner equipped with eight optical bandpass filters in the spectral range of 8-12 μm on a fixed-wing aircraft. Oil slicks may show either positive or negative thermal contrast against the surrounding sea water, depending on the sun glint conditions or the oil thickness. Classifiers were developed separately to detect oil with different contrasts by the application of backpropagation neural networks to the preprocessed radiances. Preprocessing strategies included: (1) assembly of training data through k-means clustering analysis; (2) elimination of variation in radiance magnitudes by a customized temperature correction method; (3) removal of sun glint artifacts in images by polynomial correction; and (4) extraction of the most representative features as inputs for the neural networks by a subset selection approach. The classifiers designed to detect oil with positive and negative thermal contrast relative to water achieved overall classification accuracies of 88.7 and 92.2%, respectively. Composite classification images were generated by integrating classification scores produced by the two classifiers. The prediction performance of the classification system was demonstrated through its application to images not involved during the training of the networks.
Details
- Title: Subtitle
- Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images
- Creators
- Zizi Chen - University of IowaGary W Small - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Analyst (London), Vol.147(22), pp.5018-5027
- DOI
- 10.1039/d2an01065h
- ISSN
- 0003-2654
- eISSN
- 1364-5528
- Grant note
- DOI: 10.13039/100000139, name: U.S. Environmental Protection Agency
- Language
- English
- Date published
- 09/26/2022
- Academic Unit
- Chemistry
- Record Identifier
- 9984298324902771
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