高分高光谱遥感图像计算成像：从融合到光谱超分

High-spatial-resolution hyperspectral remote sensed images can provide abundant spatial and spectral information at the same time‚ which is extremely important for practical applications such as precision agriculture‚ environmental monitoring‚ target detection and so on‚ and is one of the long-term goals in the field of remote sensing. Considering that the high-spatial resolution and high-spectral resolution are two imaging indexes mutually restricted from each other‚ it is still challenging to obtain the high-spatial-resolution hyperspectral images directly using the existing imaging technology‚ which limits its practical applicability. As one of the important technical means to reconstruct the high-spatial-resolution hyperspectral image‚ computational imaging can take the low-spatial-resolution hyperspectral image at the same time and over the same scene as a spectral priori‚ and fuse it with the spatial information provided by the high-spatial-resolution multispectral image based on the imaging model. It can also take the image-pair library or the spectral library as the priori information‚ and then reconstruct the high-spatial-resolution hyperspectral image by spectral super-resolution through spectral mapping. Here firstly‚ facing the above different ways of computational imaging for high-spatial-resolution hyperspectral images‚ a unified computational imaging model for high-spatial-resolution hyperspectral images based on prior information is constructed in this study. Then‚ according to the different sources of prior information‚ this paper summarizes the developing process and the related representative methods from the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral images‚ to the image-pair learning based spectral super-resolution for high-spatial-resolution hyperspectral images‚ and to the latest spectral library learning based spectral super-resolution for high-spatial-resolution hyperspectral images. Besides‚ the basic ideas‚ advantages and limitations of the existing algorithms are systematically analyzed. And finally‚ three possible future trends including cross-domain adaptation‚ multi-library alignment‚ and hardware implementation are analyzed and discussed in the context of the future research direction of computational imaging for the high-spatial-resolution hyperspectral image. The results show that the computational imaging of high-spatial-resolution hyperspectral remote sensed images is one of the effective ways to break through the physical limitations of the remote sensed imaging system. Incorporating fusion and spectral super-resolution into a unified framework is conducive to systematically combing different sources of prior information‚ leading to more targeted high-precision and high-stability reconstruction. This study provides a unified framework and technical means for computational imaging of high-spatial-resolution hyperspectral remote sensed images‚ clarifies the future development direction of remote sensed image fusion and spectral super-resolution‚ and is expected to furtherly improve the ability of fine structure detection and fine spectral discrimination‚ thus providing technical support for the subsequent high-precision and high-reliability spectral target detection‚ object classification and other application tasks.