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Proceedings Paper

Fusion of Radarsat SAR and ETM+ imagery for identification of fresh water wetland
Author(s): Renzong Ruan; Xuezhi Feng; Yuanjian She
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Paper Abstract

The main aim of this paper was to identify inland fresh water wetland by using RADARSAT SAR data in combination with optical remote sensing data ETM+. The test area is a part of Hongze Lake, the fourth biggest fresh water lake in China, one of important wetlands for migratory birds in China. In this paper, two scenes of RADARSAT SAR data were acquired, one was obtained (incidence angle 39.1°) on July 9, 2003, another scene of SAR acquired on July 13, 2003(incidence angle 29.8 °). Optical remotely sensed data was Landsat ETM+ acquired on August 21, 2002. In order to explore the potential of Radarsat SAR data in the differentiation of different wetland types and wetland and upland types, two schemes were designed: one scheme was that Landsat ETM+ data and its derived data such as textural metrics were used to the classification of the study area; the other is that the Landsat ETM+ data, derived ancillary data and SAR data were used. CART algorithm was selected for the generation of decision rules, and the rules were applied to the classification of landuse/cover in the whole study area. The results showed that the combination of the SAR data and the optical remotely sensed data have achieved the highest classification accuracy (92.3% of total classification accuracy). The results also confirmed the value of classification tree in the identification of fresh water wetland. It was illustrated that radar data was a good data source for the identification of wetland.

Paper Details

Date Published: 26 July 2007
PDF: 11 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675221 (26 July 2007); doi: 10.1117/12.760748
Show Author Affiliations
Renzong Ruan, Hohai Univ. (China)
Xuezhi Feng, Nanjing Univ. (China)
Yuanjian She, Hohai Univ. (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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