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 ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Zeng, Xuexing and McKee, David ORCID logoORCID: https://orcid.org/0000-0001-8023-5923;
    Item type:Book Section
    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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