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

Accurate ground control pointsx2019; (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 targetsx2019; 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 GCPsx2019; 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 GCPsx2019; 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 GCPsx2019; 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.