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Automated georeferencing and orthorectification of Amazon basin-wide SAR mosaics using SRTM DEM data

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2 Author(s)
Yongwei Sheng ; Dept. of Environ. Resources & Forest Eng., State Univ. of New York, Syracuse, NY, USA ; Alsdorf, D.E.

Frequently large synthetic aperture radar (SAR) mosaics are not precisely georeferenced because topographic distortions are not removed during the mosaicking process due to the lack of adequate digital elevation models (DEMs). The Shuttle Radar Topography Mission (SRTM) has recently provided high-resolution DEM data with nearly global coverage and makes it possible to rectify SAR mosaics. Though techniques are available for rectifying individual scenes of SAR imagery using DEM data, these methods encounter difficulties when rectifying SAR mosaics because abrupt geometric discontinuities occur in SAR mosaics at scene boundaries. This paper introduces an automated method to removing topographic distortions from SAR mosaics and producing orthorectified mosaics, without accessing original SAR images. The procedures include SAR image simulation from DEMs, two-staged image matching between SAR mosaics and the simulated image, automated tie-point derivation and screening, piecewise image rectification for localized adjustment, and production of orthorectified mosaics. The method is used to orthorectify both high-water and low-water Global Rain Forest Mapping project SAR mosaics covering the entire Amazon basin. Validation results show that one-pixel (i.e., 92 m) positioning accuracy (root mean square error) was achieved in both cases, compared to 14-16 pixel errors (i.e., 1288-1472 m) of the original mosaics.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:43 ,  Issue: 8 )