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Detail focused fusion of hyperspectral and multispectral images

  • role： First author第一作者
  • Affiliation：

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China

    Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei 230000, China

  • Email：fangshuai@163.com
  • Introduction：方帅，研究方向遥感图像处理、计算机视觉。E-mail：fangshuai@163.com
FANG Shuai12,  
  • Affiliation：

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China

YAN Mingchang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China

  • Email：zhangjing@hfut.edu.cn
  • Introduction：张晶，研究方向为人工智能、数据挖掘。E-mail：zhangjing@hfut.edu.cn
ZHANG Jing1*,  
  • Affiliation：

    Department of Automation, University of Science and Technology of China, Hefei 230072, China

CAO Yang3

Resümee

Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas.The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient.Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics.This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail.

Schlüsselwort

remote sensing;hyperspectral image;image fusion;Dictionary Learning;Edge Adaptive Directional Total Variation;Local Low Rank

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