Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

Jinchang Ren; Xuexing Zeng; David McKee

doi:10.1109/IGARSS.2015.7326268

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

Jinchang Ren, Xuexing Zeng, David McKee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

117 Downloads (Pure)

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.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Pages	2303-2306
Number of pages	4
ISBN (Print)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7326268
Publication status	Published - 31 Jul 2015
Event	IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015) - Convention Center, Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Conference

Conference	IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)
Abbreviated title	IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Keywords

ocean colour remote sensing
coastal classification
chlorophyll concentration measurement
image segmentation
multispectral/hyperspectral imaging

Access to Document

10.1109/IGARSS.2015.7326268

Ren-etal-IEEE-IGARSS-2015-Segmentation-of-multispectral-images-and-predictionAccepted author manuscript, 616 KB

Cite this

@inproceedings{f84fe671bf2f40a0af17b92c538093a0,

title = "Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing",

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.",

keywords = "ocean colour remote sensing, coastal classification, chlorophyll concentration measurement, image segmentation, multispectral/hyperspectral imaging",

author = "Jinchang Ren and Xuexing Zeng and David McKee",

note = "{\textcopyright} 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.; IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015), IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

year = "2015",

month = jul,

day = "31",

doi = "10.1109/IGARSS.2015.7326268",

language = "English",

isbn = "9781479979295",

pages = "2303--2306",

booktitle = "2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "IEEE",

}

Ren, J, Zeng, X & McKee, D 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)., 7326268, IEEE, Piscataway, NJ., pp. 2303-2306, IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015), Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7326268

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing. / Ren, Jinchang; Zeng, Xuexing; McKee, David.
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway, NJ.: IEEE, 2015. p. 2303-2306 7326268.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

AU - Ren, Jinchang

AU - Zeng, Xuexing

AU - McKee, David

N1 - © 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.

PY - 2015/7/31

Y1 - 2015/7/31

N2 - 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.

AB - 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.

KW - ocean colour remote sensing

KW - coastal classification

KW - chlorophyll concentration measurement

KW - image segmentation

KW - multispectral/hyperspectral imaging

UR - http://www.igarss2015.org/default.asp

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7303999

U2 - 10.1109/IGARSS.2015.7326268

DO - 10.1109/IGARSS.2015.7326268

M3 - Conference contribution book

SN - 9781479979295

SP - 2303

EP - 2306

BT - 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

PB - IEEE

CY - Piscataway, NJ.

T2 - IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)

Y2 - 26 July 2015 through 31 July 2015

ER -

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

Abstract

Conference

Keywords

Access to Document

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Fingerprint

IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)

Consultancy for ARGANS UK Ltd - provision of expert advice to leading remote sensing consultancy firm on a European Space Agency Water Radiance project. Named on application for second ESA project - CoastColour.

Cite this

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Activities

IEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)

Consultancy for ARGANS UK Ltd - provision of expert advice to leading remote sensing consultancy firm on a European Space Agency Water Radiance project. Named on application for second ESA project - CoastColour.

Cite this