GCPs Extraction With Geometric Texture Pattern for Thermal Infrared Remote Sensing Images

Abstract

Accurate ground control points’ (GCPs) extraction is extremely essential to support the on-orbit geometric calibration and registrations of multitemporal and multispectral remote sensing images (RSIs). However, compared with other images, the thermal infrared (TIR) images responding to the targets’ temperature usually present low spatial resolution, poor contrast, and different mapping intensity, which make it difficult to get enough precision matches in the corresponding image pairs for GCPs’ extraction. Furthermore, with more attention to the gradient properties surrounding the interest points, the conventional feature-based algorithms generally neglect the plenty of geometric textural features of RSIs. Here, in this letter, we propose an accurate geometric-texture-based GCPs’ extraction approach for TIR RSIs. The novel textural log-polar pattern and the double constrained matching rules comprising the matching bits and differences are combined to guarantee the ultimate GCPs’ accuracy. The experimental results evaluated on TIR RSIs of Landsat 8 and GLS2000 show that the absolute matching errors of the proposed method in sample and line directions can be 0.50 and 0.47 pixels, which improve a lot in terms of three state-of-the-art methods.