A stitching seams search strategy based on spectral image classification for hyperspectral image stitching

Hyperspectral image data is a form of data that combines images and spectra, and there are information differences between images in different bands when performing cube concatenation of hyperspectral data. A stitching seam search strategy based on hyperspectral spectral image classification is proposed to address the insufficient utilization of spectral dimension information in current data cube stitching methods. The main steps in searching for stitching seams are: Iteratively self-organizing data analysis algorithm (ISODATA) is used to classify two hyperspectral data cubes separately. Perform grayscale changes on the classification result images. Use graph cutting method to search for stitching seams on the transformed image. Apply the stitching seam to all bands to obtain the spliced hyperspectral data. The experimental results of applying this method to unmanned aerial hyperspectral data cubes captured by acousto-optic tunable filter (AOTF) spectral imager at waypoints show that our proposed method has certain advantages in both spatial and spectral dimensions compared to using stitching seams obtained from a single spectral segment image to achieve hyperspectral data cube stitching strategy.