Support Vector Regression-Based Downscaling for Intercalibration of Multiresolution Satellite Images

Abstract

This paper introduces a nonlinear super-resolution method for converting low spatial resolution data into high spatial resolution data to calibrate multiple sensors with a moderate spatial resolution difference, e.g., the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (30 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near infrared (NIR) sensors (15 m). A preliminary linear calibration was first applied to reduce the radiometric difference. The remaining nonlinear part of the radiometric and spatial resolution differences were then calibrated by downscaling the ETM+ data to ASTER data using a support vector regression (SVR)-based super-resolution method. Experiments were conducted on two subsets (representing rural and urban areas) of the ETM+ and ASTER scenes located in the central United States on top of atmospheric reflectance observed on August 13, 2001. It was found that the radiometric difference between the two sensors caused by their spectral band difference could be largely reduced by a linear transfer equation, and the reduction could be more than 60% for the green and NIR bands. The SVR-calibrated data showed improvement over the linearly calibrated data in terms of quantitative measures and visual analysis. Furthermore, SVR calibration improved the spatial resolution of the ETM+ data toward resembling the 15-m cell size of the ASTER pixel. Consequently, the proposed method has the potential to extend an ASTER scene's swath width to match that of an ETM+ scene.