A robust mosaicking procedure for high spatial resolution remote sensing images

With the rapid development of sensor manufacturing technology, high spatial resolution (HR) images are becoming more easily acquired and more widely used. However, it is common that a region of interest (ROI) cannot be completely acquired from a single image. Image mosaicking can resolve the problem by creating a new large-area image from multiple images with overlapping areas. A typical mosaicking procedure for HR remote sensing images includes three successive steps: tonal adjustment, seamline detection, and image blending. In this paper, we propose a robust mosaicking procedure featuring novel ideas in all three steps, which is aimed at processing HR remote sensing images of urban areas. Firstly, the tonal adjustment is realized by a local moment matching (LMM) algorithm, which solves the nonlinear photometric correlation problem between adjacent images. Secondly, an automatic piecewise dynamic program (APDP) algorithm for seamline detection is proposed to detect the optimal seamline on the overlapped area. Last but not least, we propose a cosine distance weighted blending (CDWB) method to ensure that the seamline is as invisible as possible. Compared to the state-of-the-art methods, the proposed method was proved to be effective in experiments with high resolution aerial and satellite images.