It is crucial to overcome the limitation of the computer capacity in analyzing the electromagnetic (EM) scattering from electrically large objects. The characteristic basis function method (CBFM) is a novel approach to solve this problem. In this paper, a more efficient method is presented by combining CBFM with the parallel technique. The matrix used in CBFM is rearranged to be suitable for parallel process. The parallel CBFM is applied to analyze the electromagnetic scattering feature of 3-D objects. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up the RCS calculation of large scale objects.
The fusion of the hyperspectral image (HSI) and the multispectral image (MSI) is commonly employed to obtain a high spatial resolution hyperspectral image (HR-HSI); however, existing methods often involve complex feature extraction and optimization steps, resulting in time-consuming fusion processes. Additionally, these methods typically require parameter adjustments for different datasets. Still, reliable references for parameter adjustment are often unavailable in practical scenarios, leading to subpar fusion results compared to simulated scenarios. To address these challenges, this paper proposes a fusion method based on a correlation matrix. Firstly, we assume the existence of a correlation matrix that effectively correlates the spectral and spatial information of HSI and MSI, enabling fast fusion. Subsequently, we derive a correlation matrix that satisfies the given assumption by deducing the generative relationship among HR-HSI, HSI, and MSI. Finally, we optimize the fused result using the Sylvester equation. We tested our proposed method on two simulated datasets and one real dataset. Experimental results demonstrate that our method outperforms existing state-of-the-art methods. Particularly, in terms of fusion time, our method achieves fusion in less than 0.1 seconds in some cases. This method provides a practical and feasible solution for the fusion of hyperspectral and multispectral images, overcoming the challenges of complex fusion processes and parameter adjustment while ensuring a quick fusion process.
Recently, methods for obtaining a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) and high spatial resolution multispectral image (HR-MSI) have become increasingly popular. However, most fusion methods require knowing the point spread function (PSF) or the spectral response function (SRF) in advance, which are uncertain and thus limit the practicability of these fusion methods. To solve this problem, we propose a fast fusion method based on the matrix truncated singular value decomposition (FTMSVD) without using the SRF, in which our first finding about the similarity between the HR-HSI and HR-MSI is utilized after matrix truncated singular value decomposition (TMSVD). We tested the FTMSVD method on two simulated data sets, Pavia University and CAVE, and a real data set wherein the remote sensing images are generated by two different spectral cameras, Sentinel 2 and Hyperion. The advantages of FTMSVD method are demonstrated by the experimental results for all data sets. Compared with the state-of-the-art non-blind methods, our proposed method can achieve more effective fusion results while reducing the fusing time to less than 1% of such methods; moreover, our proposed method can improve the PSNR value by up to 16 dB compared with the state-of-the-art blind methods.
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