Hyperspectral Image Stitching via Optimal Seamline Detection

Abstract

Hyperspectral images (HSIs) with both spatial and spectral information have found broad applications. Since most cameras have narrow viewing angle, generating panoramic images is essential to show a large-range view of the environment. So far, there are few studies on HSI stitching, and the stitching result still suffers from some problems, such as blurring and ghosting, geometric misalignment, visible seam, and spectral distortion. Hence, to address the above disadvantages, we propose a novel HSI stitching strategy using optimal seamline detection approach in this letter. First, we use a fast and robust seam estimation method to determine the seamline in each single band of HSI. This method works in RGB images, and we have modified it to be used in a single-band gray-scale image of HSI. Then, to guarantee the integrity of spatial and spectral information of hundreds of bands of HSI, we propose to apply the structural similarity (SSIM) index to select the optimal one among all band candidate seamlines and use the selected optimal seamline to stitch all the remaining bands. The experimental results demonstrate that our proposed approach outperforms traditional HSI stitching approach in both spatial and spectral performances.