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Comparison of fusion methods on GF-5 hyperspectral data

  • role： First author第一作者
  • Affiliation：

    National Engineering Laboratory for Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福，研究方向为高光谱数据处理技术。E-mail：zhanglf@radi.ac.cn
ZHANG Lifu1,  
  • Affiliation：

    National Engineering Laboratory for Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Xiaoyang12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Laboratory for Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：sunxj@radi.ac.cn
  • Introduction：孙雪剑，研究方向为高光谱数据融合。E-mail：sunxj@radi.ac.cn
SUN Xuejian1*,  
  • Affiliation：

    Key Laboratory of Lunar and Deep Space Exploration, Chinese Academy of Sciences, Beijing 100012, China

    National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China

    University of Chinese Academy of Sciences, Beijing 100049, China

HUANG Hai342,  
  • Affiliation：

    National Engineering Laboratory for Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

PENG Mingyuan12,  
  • Affiliation：

    National Engineering Laboratory for Remote Sensing Satellite Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

CEN Yi1,  
  • Affiliation：

    Twenty First Century Aerospace Technology Co., Ltd., Beijing 100096, China

TU Kuan5

resumen

Data fusion is an effective way to solve the limitation of hyperspectral satellites on temporal and spatial resolution. Discussing the fusion effects of different methods on GF-5 hyperspectral data is highly important for information mining and promotion application of GF-5 hyperspectral data.In this study, based on the principle that the algorithm is easy to use and suitable for generalization, six fusion methods, namely, GS (Gram-Schmidt), GSA (GS Adaptive), CNMF (Coupled Non-negative Matrix Factorization), CRISP-B, CRISP-W (Color Resolution Improvement Software Package with Butterworth or Wavelet transform), GLP (Generalized Laplacian Pyramid) are separately used to perform fusion experiments on GF-5 hyperspectral data and multispectral data from BJ-2, GF-2, and GF-1/1C/1D domestic satellites. Visual interpretation, five indicators (correlation coefficient, universal image quality index, spectral angle mapper, erreur relative globale adimensionnelle de synthèse, and peak signal-to-noise ratio), classification application, and time costs are used to comprehensively evaluate the fusion results.Results show that the fusion image series are the same and the smaller the spatial resolution difference, the better the fusion result. CRISP-B, CRISP-W, and GLP can achieve a good balance in improving spatial resolution and spectral fidelity. In terms of spatial reconstruction, GLP is slightly better and more stable, while CRISP-B and CRISP-W are more stable and effective in maintaining spectral information. The data source will have a certain effect on the fusion method. In the tasks that require high spectral fidelity, such as spectral feature information extraction and analysis, GLP is more suitable for the fusion of homologous data (such as GF-5 and GF-1/1C/1D/2). When the multi-source images (GF-5 and BJ-2) are merged, CRISP-W is preferred. CNMF has a certain degree of color distortion and takes a long time to run. GSA and GS have the worst fusion effect. The spectral retention and the spatial resolution improvement ability of GSA are more stable than those of GS. Based on a small sample, the classification effect of the CRISP-B fusion result is stable and highly accurate. The GSA fusion results are rich in spatial details. Although the spectral distortion is relatively serious, it also increases the spectral distinction of the ground objects, which is still suitable for accurately drawing buildings and roads.This study provides method decision support for the fusion of GF-5 hyperspectral data and other domestic satellite multispectral data, which is helpful for the application and promotion of GF-5 hyperspectral data.

palabra clave

hyperspectral remote sensing;GF-5;domestic satellite;data fusion;fusion method evaluation

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