Multiscale Remote Sensing Image Fusion Algorithm Based on Variational Segmentation

被引：0
|
作者：
Qin F.-Q. [1 ]
Wang L.-F. [1 ]
机构：
[1] School of Computer Science, Northwestern Polytechnical University, Xi'an, 710029, Shaanxi
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 06期
关键词：
Local weighted dynamic sparse constraint; Multiscale self-similarity; Multispectral image; Remote sensing image fusion;
D O I：
10.3969/j.issn.0372-2112.2020.06.006
中图分类号：
学科分类号：
摘要：
On the fusion of panchromatic and multispectral images, two important aspects, up-sampling of multispectral images and the difference of channel details, are ignored. For the former, the loss details of low-resolution images are estimated by using self-similar patch at different scales to improve up-sampling. For the latter, the local weighted dynamic sparse constraint is proposed based on the structural similarity between panchromatic images and spectral images in gradient domain. The new objective function based on variational method are proposed, the fidelity term and the regularization term of whose are constructed respectively according to the former and the latter. In addition, a multi-scale iterative fusion framework is presented, where the resolution of the fused image is gradually improved through iterations. The fused results of each iteration are more accurate, so the final fused image is improved. Our algorithm is compared with Brovey and other component substitution algorithms, P+XS and other variational algorithms, MTF_GLP and other multi-resolution analysis algorithms. The experimental results show that the fusion results of this algorithm have good visual effect, and the objective evaluation index is better than the average of the optimal value of all comparison algorithms. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：1084 / 1090
页数：6
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