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Intelligence fusion method research of multisource high-resolution remote sensing images

  • role： First author第一作者
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

  • Email：shyli@csu.ac.cn
  • Introduction：李盛阳(1976—　)，男，研究员，研究方向为航天地面数据系统技术，遥感信息处理技术与方法。E-mail：shyli@csu.ac.cn
LI Shengyang,  
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Wanfeng,  
  • Affiliation：

    Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

YANG Song

реферат

Intelligent fusion of multi-source high resolution remote sensing images is attracting more attention due to its extensive applications. To satisfy application requirements of multi-source high resolution remote sensing image fusion, this paper studies " on-demand” intelligent fusion of multi-source remote sensing images. Particularly, this study focuses on intelligent fusion of different temporal high-resolution remote sensing images at different resolutions. This paper presents a novel intelligent fusion approach to achieve automatic image source selection as well as fusion method recommendation. This research applies the decision tree algorithm to high resolution remote sensing image fusion. The decision tree is trained to build the knowledge base of fusion rules, which is off-line updated. Each training sample consists of remote sensing images, fusion methods, and the evaluation results. Besides, to solve the problem of spectral distortion cause by hyperspherical color space transform, this paper proposes a fusion method renamed as Curvelet-HCS based on the second generation Curvelet transform and hyperspherical color space transform. The main idea of Curvelet-HCS is as follows. Curvelet-HCS decompose the image into low and high frequency components and employ different fusion rules for these two components. In detail, for the low frequency component, low frequency coefficients are computed as a weighted sum of low frequency coefficients of two fusing images, where weights are defined as functions of regional standard deviation and the local direction information entropy. Similarly, for the high frequency component, the high frequency coefficients are determined by the maximum. This research experiment the Curvelet-HCS method with different sources of satellite images, including the GF-1 and GF-2 satellite multispectral and panchromatic images. To evaluate the fusion performance of the proposed approach objectively, the fused images are quantitatively analyzed based on the mean, the standard deviation, the correlation coefficient, the information entropy and the average gradient. The results show that the image has a better visual effect after fusion, and the proposed fusion method achieves better performance compared to other fusion methods including Principal Component Analysis, Gramm-Schmidt, and Hyperspherical Color Sharpening. This thesis presents an intelligent fusion method by studying the relationship among the remote sensing images, the fusion methods and the evaluation results. This fusion approach improves the level of automation and intelligence of multi-resource high resolution remote sensing image fusion. It is a beneficial exploration for researching the intelligent on-demand service pattern. Also, this paper proposes a Curvelet-HCS fusion method which can fuse more than three bands of MS image at one time. The experimental results demonstrate that the proposed method is an effective method for fusing multispectral image and panchromatic image, and obtains good fused images.

ключеви́че слова́

high resolution;remote sensing image;intelligent fusion;decision tree;fusion method;fusion rules

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