首页 >  2020, Vol. 24, Issue (4) : 379-387

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全文摘要次数: 1689 全文下载次数: 1630
引用本文:

DOI:

10.11834/jrs.20209214

收稿日期:

2019-06-21

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基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合
孟祥超1,孙伟伟2,任凯2,杨刚2,邵枫1,符冉迪1
1.宁波大学 信息科学与工程学院, 宁波 315211;2.宁波大学 地理与空间信息技术系, 宁波 315211
摘要:

针对空间分辨率比率较大尺度差异下的高分五号(GF-5)与高分一号(GF-1)卫星影像的空—谱融合问题,提出多传感器影像融合策略:一方面,通过现有空—谱融合方法的分步融合得到融合影像;另一方面,在分步融合理论基础上,推导得出一体化融合基础框架,并进一步提出基于多分辨率分析的多传感器一体化融合方法,缓解现有方法因空间分辨率比率过大导致影像空、谱互补信息难以有效集成的问题。其中,提出的一体化融合方法基于调制传递函数MTF(Modulation Transfer Function)滤波对多传感器影像空间(高频)和光谱(低频)分量进行分解提取,并充分考虑多传感器高空间分辨率影像与高光谱分辨率影像之间的关系,以及高光谱分辨率影像波段间关系,设计合理的融合权重,最终可得到具有最高空间分辨率和最高光谱分辨率的融合影像。通过GF-1全色影像、GF-1多光谱影像、GF-5高光谱影像数据对提出方法进行实验验证,结果表明:本文方法可有效集成多传感器影像间的空、谱互补信息,得到较优融合结果。

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

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.

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