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摘要
Objective Hyperspectral images (HSI) are widely used in image classification and target detection because of their rich and useful spectral information. Their spatial resolution is required to be optimized further due to the limitation of imaging cameras. Multispectral or panchromatic images with lower spectral resolution have higher spatial resolution compared to hyperspectral images. To improve the spatial resolution of hyperspectral images, they are often fused with multispectral images (MSI) or panchromatic images in the same scene. Dictionary learning is one of the popular algorithms for image fusion, which can be divided into dictionary-spectral learning and dictionary-spatial learning. Using spectral dictionary learning algorithms, high-resolution hyper-spectral images can be expressed as an over-completed spectral dictionary multiplied by sparse coefficients. Spectral dictionary can illustrate the spectral information of high-resolution hyperspectral images, which is originated from k-singular value decomposition (SVD) and the algorithms-related. It is challenged of spatial information loss for spectral dictionary cannot fully express spatial information. Therefore, we develop a new compensated framework to explore more detailed spatial information. To improve the spatial resolution of fusion result, residual spatial information is used to compensate the preliminary result. The residual information is calculated as the error between the image obtained by spectral down sampling through preliminary results and the multispectral image. It is required to inject the residual space information into the preliminary results in an appropriate manner. However, most of the algorithms have limitations for that they only consider the differences between spectral channels but do not consider the differences in spatial. Therefore, the spectral and spatial quality of the fused image will be seriously affected if the extracted errors are injected indiscriminately into a channel. Method Our method is focused on improving the spatial resolution of hyperspectral image while keeping its spectral information free from distortion. On the basis of fully capturing the spectral information of hyperspectral image through dictionary learning, the residual spatial information is used to compensate for the spatial resolution. First, a fusion method based on local region is designed. To avoid the spectral distortion caused by inappropriate information injection, adjusting the injection degree of residual information adaptively through a coefficient in accordance with the spectral features of the local area. Second, to maintain the consistency of the spatial structure of the fusion results with multispectral image (MSI), the spatial structure of MSI is extracted in the gradient domain, and a variational model is constructed. The coefficients-calculated are incorporated to the spectral dictionary-derived preliminary results to form an optimization term updated alternately. The objective function also contains spectral constraints composed of the target image and HSI, as well as spatial constraint composed of variational components. This function is run iteratively via alternating direction method of multipliers (ADMM). The spatial and spectral constraints-involved fusion model cannot only improve the spatial resolution, but also ensure that the spectral information does not have distortion. Result Our analysis is compared to six other algorithms on two public datasets. To verify the effectiveness and efficiency of our method, qualitative and quantitative evaluation is carried out in combination with other methods-related through the experimental platform—MAT-LAB R2018a. For qualitative analysis, our proposed method is capable to get the target image-fused with higher natural and clear visual effect. For quantitative evaluation indicators, compared with the sub-optimal experiment, the Pavia University data set-relevant experimental results are reduced by 4. 2%, 4. 1% and 2. 2% in relation to relative dimensionless global error in synhesis (ERGAS), spectral angle mapper (SAM) and root mean square error (RMSE) indicators. The AVIRIS (airborne visible infrared imaging spectrometer) dataset-related values are reduced by 2. 0%, 4. 0% and 3. 5% of each. Conclusion Our fusion algorithm can effectively improve the spatial resolution preserve spectral information. Furthermore, our algorithm has its optimization and robustness potentially.
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