Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain

Nguyen Van Trung; Jung Hyun Choi; Joong Sun Won

doi:10.1117/12.869413

Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain

Nguyen Van Trung, Jung Hyun Choi, Joong Sun Won

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

1 Citation (Scopus)

Abstract

The landcover of the northern floodplain around the Tonle Sap Lake involves the various vegetations, lacustrine lands, as well as settlements. In order to understand the contribution of landcover in this area for agricultural, piscicultural activity, and environmental protection, landcover classes should be classified by using remote sensing data. The aim of this study is to increase distinction between landcover classes for classification purpose. To improve the feature texture for pre-classification data, the ALOS PALSAR is fused with ASTER data. Both data are acquired in dry season in which the vegetation is little influenced by flooding. The fused data is created by injecting the feature texture of ALOS PALSAR into ASTER data. However, spectral character is distorted due to mixed spectrum. This is reduced by choosing optimal fused algorithm. The ten landcover classes are selected as signatures to classify and calculate confusion matrixes. Those confusion matrixes reveal that the distinction between the landcover classes in fused data is better than that in ASTER data.

Original language	English
Title of host publication	Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment
DOIs	https://doi.org/10.1117/12.869413
Publication status	Published - 2010
Event	Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment - Incheon, Korea, Republic of Duration: 2010 Oct 13 → 2010 Oct 14

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7858
ISSN (Print)	0277-786X

Other

Other	Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment
Country/Territory	Korea, Republic of
City	Incheon
Period	10/10/13 → 10/10/14

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.869413

Cite this

@inproceedings{603d1994b0984e58a7b1b9e021f928a2,

title = "Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain",

abstract = "The landcover of the northern floodplain around the Tonle Sap Lake involves the various vegetations, lacustrine lands, as well as settlements. In order to understand the contribution of landcover in this area for agricultural, piscicultural activity, and environmental protection, landcover classes should be classified by using remote sensing data. The aim of this study is to increase distinction between landcover classes for classification purpose. To improve the feature texture for pre-classification data, the ALOS PALSAR is fused with ASTER data. Both data are acquired in dry season in which the vegetation is little influenced by flooding. The fused data is created by injecting the feature texture of ALOS PALSAR into ASTER data. However, spectral character is distorted due to mixed spectrum. This is reduced by choosing optimal fused algorithm. The ten landcover classes are selected as signatures to classify and calculate confusion matrixes. Those confusion matrixes reveal that the distinction between the landcover classes in fused data is better than that in ASTER data.",

author = "{Van Trung}, Nguyen and Choi, {Jung Hyun} and Won, {Joong Sun}",

year = "2010",

doi = "10.1117/12.869413",

language = "English",

isbn = "9780819483881",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment",

note = "Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment ; Conference date: 13-10-2010 Through 14-10-2010",

}

Van Trung, N, Choi, JH & Won, JS 2010, Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain. in Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment., 785815, Proceedings of SPIE - The International Society for Optical Engineering, vol. 7858, Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment, Incheon, Korea, Republic of, 10/10/13. https://doi.org/10.1117/12.869413

Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain. / Van Trung, Nguyen; Choi, Jung Hyun; Won, Joong Sun.
Remote Sensing of the Coastal Ocean, Land, and Atmosphere Environment. 2010. 785815 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7858).

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

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T1 - Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain

AU - Van Trung, Nguyen

AU - Choi, Jung Hyun

AU - Won, Joong Sun

PY - 2010

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N2 - The landcover of the northern floodplain around the Tonle Sap Lake involves the various vegetations, lacustrine lands, as well as settlements. In order to understand the contribution of landcover in this area for agricultural, piscicultural activity, and environmental protection, landcover classes should be classified by using remote sensing data. The aim of this study is to increase distinction between landcover classes for classification purpose. To improve the feature texture for pre-classification data, the ALOS PALSAR is fused with ASTER data. Both data are acquired in dry season in which the vegetation is little influenced by flooding. The fused data is created by injecting the feature texture of ALOS PALSAR into ASTER data. However, spectral character is distorted due to mixed spectrum. This is reduced by choosing optimal fused algorithm. The ten landcover classes are selected as signatures to classify and calculate confusion matrixes. Those confusion matrixes reveal that the distinction between the landcover classes in fused data is better than that in ASTER data.

AB - The landcover of the northern floodplain around the Tonle Sap Lake involves the various vegetations, lacustrine lands, as well as settlements. In order to understand the contribution of landcover in this area for agricultural, piscicultural activity, and environmental protection, landcover classes should be classified by using remote sensing data. The aim of this study is to increase distinction between landcover classes for classification purpose. To improve the feature texture for pre-classification data, the ALOS PALSAR is fused with ASTER data. Both data are acquired in dry season in which the vegetation is little influenced by flooding. The fused data is created by injecting the feature texture of ALOS PALSAR into ASTER data. However, spectral character is distorted due to mixed spectrum. This is reduced by choosing optimal fused algorithm. The ten landcover classes are selected as signatures to classify and calculate confusion matrixes. Those confusion matrixes reveal that the distinction between the landcover classes in fused data is better than that in ASTER data.

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Fusion of ALOS PALSAR and ASTER data for landcover classification at Tonle Sap floodplain

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