Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)

Dr. Yu Liu; Chun-xiang Shi; Xue-Gong Liu

Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)

Dr. Yu Liu * §

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Chun-xiang Shi §

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Xue-Gong Liu §

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shicx §

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zk §

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§ Tianjin Hydraulic Research Institute

Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)

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Abstract

This paper presents a new algorithm Artificial Neural Network and Seeded Region Growing (ANN-SRG) to segment cloud patches of different types. This method used Seeded Region Growing (SRG) as segmentation algorithm, and Artificial Neural Network (ANN) Cloud classification as preprocessing algorithm. It can be trained to respond favorably to cloud types of interest, and SRG method is no longer sensitive to the seeds selection and growing rule. To illustrate the performance of this technique, this paper applied it on Chinese first operational geostationary meteorological satellite FengYun-2C (FY-2C) in three infrared channels (IR1, IR2,) with 2864 samples collected by meteorologists in June, July, and August in 2007. The result shows that this method can distinguish and segment cloud patches of different types, and improves the traditional SRG algorithm by reducing the uncertainty of seeds extraction and regional growth.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Yu Liu, Chun-xiang Shi, Xue-Gong Liu, shicx, zk. . "Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)". Global Journal of Computer Science and Technology GJCST Volume 12 (GJCST Volume 12 Issue 7).

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GJCST Volume 12 Issue 7

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Artificial Neural Network cloud patch cloud type FY-2C multi-channel satellite image Seeded Region Growing segmentation

Classification

GJCST Classification I.2.1

Submission ReceivedApril 12, 2012
Peer Review Double Blind
Handling Editor
Accepted May 5, 2012
Published April 4, 2012

Version of record

v1.2

Issue date

April 4, 2012

Language

English

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Article Matrices

Total Score: 180

Country: China

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Yu Liu, Du-Gang Xi, Xue-Gong Liu, Chun-xiang Shi, Kai Zhang (PhD/Dr. count: 1)

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Publish Date: 2012 05, Tue

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Total Views: 5.1K

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