The fusion of hyperspectral and multispectral images is an important strategy for enhancing the spatial resolution of hyperspectral images. With the rapid advancement of multispectral imaging technology, the disparity in spatial resolution between multispectral and hyperspectral images is increasing. In certain scenarios, termed high-magnification, this difference can exceed 32×. Previous methods do not perform well under high-magnification fusion, and naturally, a challenge arises in achieving effective high-magnification super-resolution fusion. In light of the above analysis, this paper introduces a novel algorithm for high-magnification super-resolution fusion of hyperspectral and multispectral images based on the joint optimization of spatial and spectral information. Specifically, our algorithm consists of three stages: 1) a fast preliminary fusion stage based on the Moore-Penrose inverse and singular value correlation priors for the rapid acquisition of preliminary solutions, 2) a joint spatial-spectral optimization stage where a coupled optimization framework is constructed to achieve integrated optimization of spatial and spectral information, and 3) an error backpropagation optimization stage where an effective error optimization term is introduced to further refine the fusion performance. We conducted extensive experiments on widely employed publicly available simulated datasets and real datasets. The experimental results unequivocally indicate that our proposed methodology consistently exhibits superior fusion performance compared with state-of-the-art methods, even under the condition of ≥60× super-resolution.

The fusion of low spatial resolution hyperspectral images and high spatial resolution multispectral images in the same scenario is important for the super-resolution of hyperspectral images. The spectral response function (SRF) and the point spread function (PSF) are two crucial prior pieces of information in fusion, and most of the current algorithms need to provide these two preliminary pieces of information in advance, even for semi-blind fusion algorithms at least the SRF. This causes limitations in the application of fusion algorithms. This paper aims to solve the dependence of the fusion method on the point spread function and proposes a method to estimate the spectral response function from the images involved in the fusion to achieve blind fusion. We conducted experiments on simulated datasets Pavia University, CAVE, and the remote sensing images acquired by two spectral cameras, Sentinel 2 and Hyperion. The experimental results show that our proposed SRF estimation method can improve the PSNR value by 5 dB on average compared with other state-of-the-art SRF estimation results. The proposed blind fusion method can improve the PSNR value of fusion results by 3–15 dB compared with other blind fusion methods.

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Fusion low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI) are important methods for obtaining high-resolution hyperspectral images (HR-HSI). Some hyperspectral image fusion application areas have strong real-time requirements for image fusion, and a fast fusion method is urgently needed. This paper proposes a fast and stable fusion method (FSF) based on matrix factorization, which can largely reduce the computational workloads of image fusion to achieve fast and efficient image fusion. FSF introduces the Moore–Penrose inverse in the fusion model to simplify the estimation of the coefficient matrix and uses singular value decomposition (SVD) to simplify the estimation of the spectral basis, thus significantly reducing the computational effort of model solving. Meanwhile, FSF introduces two multiplicative iterative processes to optimize the spectral basis and coefficient matrix to achieve stable and high-quality fusion. We have tested the fusion method on remote sensing and ground-based datasets. The experiments show that our proposed method can achieve the performance of several state-of-the-art algorithms while reducing execution time to less than 1% of such algorithms.

Semi-blind fusion of hyperspectral images (HSI) and multispectral images (MSI) is a critical technique for generating high-resolution hyperspectral images (HR-HSI) without the need for complex point spread function (PSF) estimation. Despite the broad application potential of semi-blind fusion algorithms, existing methods face three major challenges. First, as imaging technology advances, the spatial resolution gap between hyperspectral images (HSI) and multispectral images (MSI) continues to widen, making high-magnification fusion increasingly urgent. Second, especially for deep learning-based methods, existing algorithms require meticulous hyperparameter tuning to enhance fusion quality. Third, the complex fusion process impedes the speed of image fusion. To address these challenges, we propose a parameter-free spectral-spatial optimization algorithm specifically designed to handle high-magnification differences in the semi-blind fusion of HSI and MSI. This method enables fast fusion using simple matrix operations and consists of three main steps: (1) rapidly computing an initial solution using the Moore-Penrose inverse of the spatial response function (SRF), (2) constructing spectral errors from the initial solution to effectively extract spectral information, and (3) reconstructing spatial details using MSI to form spatial errors, thereby accurately reconstructing HR-HSI for high-quality fusion. Comparative experiments on two simulated and three real datasets with state-of-the-art algorithms demonstrate that our proposed semi-blind fusion method not only achieves 64X high-magnification fusion but also reduces the computation time to just 23.9%-49.5% of that required by the fastest competing methods. The code is available at https://github.com/Long-ji/PFSSOA.

A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.