Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing
Ren, Jinchang and Zeng, Xuexing and McKee, David; (2015) Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, ITA, pp. 2303-2306. ISBN 9781479979295 (https://doi.org/10.1109/IGARSS.2015.7326268)
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Abstract
With the development of new sensors and data processing techniques, ocean colour remote sensing has undergone rapid development in more accurately measurement of coastal shelf classification and concentration of chlorophyll. In this paper, multispectral images are employed to achieve these targets, using techniques including region-growing based segmentation for pixel classification and support vector regression for ChI-a prediction. Interesting results are reported to show the great potential in using state-of-the-art data analysis techniques for effective ocean colour remote sensing.
ORCID iDs
Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Zeng, Xuexing and McKee, David ORCID: https://orcid.org/0000-0001-8023-5923;-
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Item type: Book Section ID code: 54443 Dates: DateEvent31 July 2015Published3 April 2015AcceptedNotes: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Faculty of Science > PhysicsDepositing user: Pure Administrator Date deposited: 02 Oct 2015 11:22 Last modified: 11 Nov 2024 15:00 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/54443