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Abstract:Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information.

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The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smart-fluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smart-fluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make best efforts to reach the user's requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smart-fluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model. CCS CONCEPTS • Computing methodologies → Parallel algorithms; Neural networks; Model development and analysis.

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The hyperspectral pansharpening is a significant preprocessing technology in hyperspectral images application. A new optimized injection model-based hyperspectral pansharpening algorithm is proposed in this paper. Compared with the traditional pansharpening methods, the algorithm achieves two major improvements: 1) the total injected spatial information is obtained by integrating the spatial components of hyperspectral (HS) and panchromatic (PAN) images by PCA transformation; and 2) the gain matrix proposed in this paper is composed of two factors which constraint spectral and spatial distortions respectively. Specifically, the morphological open-closing operation and Laplacian of Gaussian enhancement scheme are used for denoising the interpolated HS and PAN images, respectively. Then, the spatial components of the denoised HS and PAN images are respectively extracted by the morphological gradient operation and homomorphic filtering. The PCA transform is applied to the results to obtain the first principal component served as total spatial details. The total spatial information weighted by the gain matrix is finally combined with the interpolated HS images to generate the pan-sharpened images, in which a new gain matrix is constructed to minimize the spectral and spatial distortions. The extensive experiments have demonstrated the potential of the proposed method in balancing spectral preservation and spatial sharpness. INDEX TERMS Hyperspectral pansharpening, hyperspectral image, panchromatic image, spatial information, gain matrix.