Seamless UAV Hyperspectral Image Stitching Using Optimal Seamline Detection via Graph Cuts.

Image stitching task targets to derive a large panoramic image for obtaining extensive information. However, artifacts such as ghosting or geometric misalignment are inevitably generated. As a practical measure, optimal seamline detection strategies use the spatial information to obtain the optimal seam in RGB image stitching, but they cannot be directly used in hyperspectral image (HSI) stitching. Since the spatial information of numerous continuous bands of HSI is different, the detected seam of the traditional RGB-based method in each band of HSI is divergent, which will cause visual difference and spectral distortion. To solve this problem, we propose a novel optimal seamline detection strategy via graph cuts for HSI stitching in this work. First, we use robust feature matching and elastic warp to align multiple adjacent images into a common geometrical transformation. After that, we design a novel energy function composing both the spatial and spectral information of HSI to determine an optimal seam in continuous regions with high texture consistency. Finally, we use the graph cuts method to eliminate visible artifacts. Our method can determine a unique optimal seam in the whole HSI for stitching so as to obtain high-quality panoramic HSI without artifacts and reduce the spectral distortion. A series of experiments verify the effectiveness and superiority of the proposed method to several advanced approaches in HSI stitching.