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Spatial-spectral fusion of GF-5/GF-1 remote sensing images based on multiresolution analysis

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：mengxiangchao@nbu.edu.cn
  • Introduction： 孟祥超，1989年生, 男, 讲师, 研究方向为遥感影像融合理论方法和应用。 E-mail:mengxiangchao@nbu.edu.cn
MENG Xiangchao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：sunweiwei@nbu.edu.cn
  • Introduction： 孙伟伟,1985年生， 男, 教授, 研究方向为地理信息系统理论和方法、高光谱遥感理论和海岸带监测技术。E-mail:sunweiwei@nbu.edu.cn
SUN Weiwei2*,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

REN Kai2,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

YANG Gang2,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

SHAO Feng1,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

FU Randi1

Resümee

This study proposed a multisensor image fusion solution for the GF-5/GF-1 spatial-spectral fusion with large spatial resolution ratio. We aimed to obtain the fused image through step-by-step fusion of multisensor remote sensing images. A unified fusion framework for multisensor image fusion was derived on the basis of step-by-step fusion theory. An integrated multisensor image fusion method based on multiresolution analysis theory was proposed in accordance with the unified framework. The proposed method can overcome the difficulty of integrating complementary high spatial and spectral information of multisource images under high spatial resolution ratio. In the proposed method, a modulation transfer function was applied to separate the spatial (high frequency) and spectral components (low frequency) of multisource images. The fusion weight was constructed by comprehensively considering the relationship between multisensor high spatial resolution images and high spectral resolution images and the relationship among the spectral bands of the high spectral resolution image. Fused images with the highest spatial and spectral resolutions can be obtained. The GF-1 panchromatic, GF-1 multispectral, and GF-5 hyperspectral images were used in the experiments. Experimental results show that the proposed multisensor spatial–spectral fusion can effectively integrate the complementary spatial and spectral information to obtain the comparative fused results.

Schlüsselwort

remote sensing;GF-5 satellite;spatial-spectral fusion;large spatial resolution difference;multi-sensor

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