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 (In Press)
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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.
Creators(s): |
Ren, Jinchang ![]() ![]() | Item type: | Book Section |
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ID code: | 54443 |
Notes: | © 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. |
Keywords: | ocean colour remote sensing, coastal classification, chlorophyll concentration measurement, image segmentation, multispectral/hyperspectral imaging, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering |
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 > Physics |
Depositing user: | Pure Administrator |
Date deposited: | 02 Oct 2015 11:22 |
Last modified: | 20 Jan 2021 15:44 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/54443 |
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