Abstract
This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.
Highlights
synthetic aperture radar (SAR) has great significance because of its imaging capability in all day and all weather which could make up for the weakness of optical remote sensing in the application of land use and dynamic monitoring in cloudy and rainy areas
This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM
The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification
Summary
SAR has great significance because of its imaging capability in all day and all weather which could make up for the weakness of optical remote sensing in the application of land use and dynamic monitoring in cloudy and rainy areas. The smallest processing unit of object-oriented classification method in information extraction is no longer the pixel, but the object with more semantic information of adjacent pixels. It classifies the remote sensing images in a higher level in order to reduce the semantic information loss rate as in the traditional pixel-based classification; so that the classification results semantic information will be richer [1]. A lot of scholars from home and abroad have studied the land use and land-cover classification using optical images based on object-oriented technology. This paper tries to conduct the double-polarized SAR image classification based on object-oriented technology, and confirms that the method is suitable for the high-precision classification of dual-polarized SAR images
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