Efficient Geometric Correction Workflow for Airborne Hyperspectral Images through DEM-Driven Correction Techniques

Abstract

Geometric correction, a pivotal step in the preprocessing of airborne remote sensing imagery, is critical for ensuring the accuracy of subsequent quantitative analyses. Achieving precise and efficient geometric correction for airborne hyperspectral data remains a significant challenge in the field. This study presents a new method for system-level and fine-scale geometric correction of uncontrolled airborne images utilizing DEM data, which integrates forward and inverse transformation algorithms. Furthermore, an optimized workflow is proposed to facilitate the processing of large-scale hyperspectral datasets. The effectiveness of the proposed method is demonstrated through an application analysis using airborne HyMap imagery, with experimental outcomes indicating high application accuracy and enhanced processing efficiency.