Landcover classification using ERS SAR/INSAR data over tropical areas

Nicolas Stussi; Soo Chin Liew; Leong Keong Kwoh; Hock Lim; Janet Elizabeth Nichol; Kim Chuan Goh

Landcover classification using ERS SAR/INSAR data over tropical areas

Nicolas Stussi, Soo Chin Liew, Leong Keong Kwoh, Hock Lim, Janet Elizabeth Nichol, Kim Chuan Goh

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

In this study, we present a classification method using both optical and SAR data in order to perform landcover classification. First, different relevant parameters are derived from ERS-SAR data using multitemporal and interferometric analysis. Optical data are then used to define a set of training areas in order to perform a supervised classification. The results of classification using these techniques are compared with existing landuse maps and information derived from SPOT and Landsat data. We point out the usefulness of the coherence γtandemcomponent for this application. Moreover, we show that Δγ (difference between γtandemand γ35-day) can give additional useful information for the classification process.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	IEEE
Pages	813-815
Number of pages	3
Publication status	Published - 1 Jan 1997
Externally published	Yes
Event	Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 1 (of 4) - Singapore, Singapore Duration: 3 Aug 1997 → 8 Aug 1997

Conference

Conference	Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 1 (of 4)
Country/Territory	Singapore
City	Singapore
Period	3/08/97 → 8/08/97

ASJC Scopus subject areas

Computer Science Applications
Earth and Planetary Sciences(all)

Cite this

@inproceedings{96fb8458bfd4434884c5345743030e25,

title = "Landcover classification using ERS SAR/INSAR data over tropical areas",

abstract = "In this study, we present a classification method using both optical and SAR data in order to perform landcover classification. First, different relevant parameters are derived from ERS-SAR data using multitemporal and interferometric analysis. Optical data are then used to define a set of training areas in order to perform a supervised classification. The results of classification using these techniques are compared with existing landuse maps and information derived from SPOT and Landsat data. We point out the usefulness of the coherence γtandemcomponent for this application. Moreover, we show that Δγ (difference between γtandemand γ35-day) can give additional useful information for the classification process.",

author = "Nicolas Stussi and Liew, {Soo Chin} and Kwoh, {Leong Keong} and Hock Lim and Nichol, {Janet Elizabeth} and Goh, {Kim Chuan}",

year = "1997",

month = jan,

day = "1",

language = "English",

pages = "813--815",

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

publisher = "IEEE",

address = "United States",

note = "Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 1 (of 4) ; Conference date: 03-08-1997 Through 08-08-1997",

}

Landcover classification using ERS SAR/INSAR data over tropical areas. / Stussi, Nicolas; Liew, Soo Chin; Kwoh, Leong Keong; Lim, Hock; Nichol, Janet Elizabeth; Goh, Kim Chuan.

International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 1997. p. 813-815.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Landcover classification using ERS SAR/INSAR data over tropical areas

AU - Stussi, Nicolas

AU - Liew, Soo Chin

AU - Kwoh, Leong Keong

AU - Lim, Hock

AU - Nichol, Janet Elizabeth

AU - Goh, Kim Chuan

PY - 1997/1/1

Y1 - 1997/1/1

N2 - In this study, we present a classification method using both optical and SAR data in order to perform landcover classification. First, different relevant parameters are derived from ERS-SAR data using multitemporal and interferometric analysis. Optical data are then used to define a set of training areas in order to perform a supervised classification. The results of classification using these techniques are compared with existing landuse maps and information derived from SPOT and Landsat data. We point out the usefulness of the coherence γtandemcomponent for this application. Moreover, we show that Δγ (difference between γtandemand γ35-day) can give additional useful information for the classification process.

AB - In this study, we present a classification method using both optical and SAR data in order to perform landcover classification. First, different relevant parameters are derived from ERS-SAR data using multitemporal and interferometric analysis. Optical data are then used to define a set of training areas in order to perform a supervised classification. The results of classification using these techniques are compared with existing landuse maps and information derived from SPOT and Landsat data. We point out the usefulness of the coherence γtandemcomponent for this application. Moreover, we show that Δγ (difference between γtandemand γ35-day) can give additional useful information for the classification process.

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M3 - Conference article published in proceeding or book

SP - 813

EP - 815

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - IEEE

T2 - Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 1 (of 4)

Y2 - 3 August 1997 through 8 August 1997

ER -

Landcover classification using ERS SAR/INSAR data over tropical areas

Abstract

Conference

ASJC Scopus subject areas

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Cite this