融合图像的频率定量分析及其空间分辨率的确定方法

doi:10.11947/j.AGCS.2016.20150146

测绘学报 ›› 2016, Vol. 45 ›› Issue (6): 691-697.doi: 10.11947/j.AGCS.2016.20150146

融合图像的频率定量分析及其空间分辨率的确定方法

段伟, 闫利

武汉大学测绘学院, 湖北武汉 430079

收稿日期:2015-03-19 修回日期:2016-04-06 出版日期:2016-06-20 发布日期:2016-06-29
作者简介:段伟(1987-),男,博士生,研究方向为遥感信息处理与分析。E-mail:Duanww08@163.com
基金资助:
国家科技支撑计划(2012BAJ23B03)

Quantitative Analysis in Frequency Domain and Determination of Spatial Resolution in Image Fusion

DUAN Wei, YAN Li

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2015-03-19 Revised:2016-04-06 Online:2016-06-20 Published:2016-06-29
Supported by:
The National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2012BAJ23B03)

摘要/Abstract

摘要： 图像融合是获取高质量图像的重要步骤之一,本文首先在频域中进行公式推导,根据高采样率奈奎斯特频率、低采样率奈奎斯特频率和信号频率三者之间的关系给出了图像融合前后信号频率的变化规律,然后以此为依据对融合图像的频率进行定量分析并确定图像空间分辨率,研究认为高分辨率图像中频率位于高采样率奈奎斯特频率和低采样率奈奎斯特频率之间的频率信息为高频信息。最后结合常用的融合方法和遥感图像对本文提出的观点进行验证,试验结果表明本文得到的结论与试验中的结果是符合的。

关键词: 图像融合, 信号频率, 分辨率, 地物频率, 傅里叶变换, 奈奎斯特频率

Abstract: Image fusion is one of the important steps to obtain high quality images.In this paper, the variation of signal frequency in image fusion is given according to the relationship between Nyquist frequency of high sampling rate,Nyquist frequency of low sampling rate and signal frequency.Then the frequency of the fused images is quantifying and the spatial resolution of the image is determined based on the the variation of signal frequency in image fusion.The high frequency information in the high resolution image is the information with the frequency between the Nyquist frequency of high sampling rate and the Nyquist frequency of low sampling rate.Finally, some fusion methods and remote sensing images are used to validate the proposed method. The experimental results show that the results obtained are consistent with the results of the experiment.

Key words: image fusion, signal frequency, resolution, object frequency, Fourier transform, Nyquist frequency

中图分类号:

P237

段伟, 闫利. 融合图像的频率定量分析及其空间分辨率的确定方法[J]. 测绘学报, 2016, 45(6): 691-697.

DUAN Wei, YAN Li. Quantitative Analysis in Frequency Domain and Determination of Spatial Resolution in Image Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(6): 691-697.

参考文献

[1] AIAZZI B, BARONTI S, LOTTI F, et al. A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2):302-306.
[2] CHOI J, YEOM J, CHANG A, et al. Hybrid Pansharpening Algorithm for High Spatial Resolution Satellite Imagery to Improve Spatial Quality[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):490-494.
[3] 李军, 周月琴, 李德仁. 影像局部直方图匹配滤波技术用于遥感影像数据融合[J]. 测绘学报, 1999, 28(3):226-232. LI Jun, ZHOU Yueqin, LI Deren. A New Fusion Approach Based on Local Histogram Matching Filtering Techniques[J]. Acta Geodaetica et Cartographica Sinica, 1999, 28(3):226-232.
[4] RAHMANI S, STRAIT M, MERKURJEV D, et al. An Adaptive IHS Pan-sharpening Method[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4):746-750.
[5] AIAZZI B, BARONTI S, SELVA M. Improving Component Substitution Pansharpening through Multivariate Regression of MS+Pan Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10):3230-3239.
[6] LI Shutao, YIN Haitao, FANG Leyuan. Remote Sensing Image Fusion via Sparse Representations over Learned Dictionaries[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9):4779-4789.
[7] JIANG Cheng, ZHANG Hongyan, SHEN Huanfeng, et al. Two-step Sparse Coding for the Pan-sharpening of Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5):1792-1805.
[8] ZHU Xiaoxiang, BAMLER R. A Sparse Image Fusion Algorithm with Application to Pan-sharpening[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5):2827-2836.
[9] DURAN J, BUADES A, COLL B, et al. A Nonlocal Variational Model for Pansharpening Image Fusion[J]. Siam Journal on Imaging Sciences, 2014, 7(2):761-796.
[10] CHOI J, YEOM J, CHANG A, et al. Hybrid Pansharpening Algorithm for High Spatial Resolution Satellite Imagery to Improve Spatial Quality[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):490-494.
[11] YIN Haitao, LI Shutao. Pansharpening with Multiscale Normalized Nonlocal Means Filter:a Two-step Approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10):5734-5745.
[12] 丰明博, 刘学, 赵冬. 多/高光谱遥感图像的投影和小波融合算法[J]. 测绘学报, 2014, 43(2):158-163. FENG Mingbo, LIU Xue, ZHAO Dong. A Fusion Method of Hyperspectral and Multispectral Images Based on Projection and Wavelet Transformation[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(2):158-163.
[13] 窦闻, 陈云浩, 何辉明. 光学遥感影像像素级融合的理论框架[J]. 测绘学报, 2009, 38(2):131-137. DOU Wen, CHEN Yunhao, HE Huiming. Theoretical Framework of Optical Remotely Sensed Image Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(2):131-137.
[14] NUNEZ J, OTAZU X, FORS O, et al. Multiresolution-based Image Fusion with Additive Wavelet Decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(3):1204-1211.
[15] PETROVIC V S, XYDEAS C S. Gradient-based Multiresolution Image Fusion[J]. IEEE Transactions on Image Processing, 2004, 13(2):228-237.
[16] 陈浩, 王延杰. 基于拉普拉斯金字塔变换的图像融合算法研究[J]. 激光与红外, 2009, 39(4):439-442. CHEN Hao, WANG Yanjie. Research on Image Fusion Algorithm Based on Laplacian Pyramid Transform[J]. Laser & Infrared, 2009, 39(4):439-442.
[17] SHAH V P, YOUNAN N H, KING R L. An Efficient Pan-sharpening Method via a Combined Adaptive PCA Approach and Contourlets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5):1323-1335.
[18] LEWIS J J, O'CALLAGHAN R J, NIKOLOV S G, et al. Pixel-and Region-based Image Fusion with Complex Wavelets[J]. Information Fusion, 2007, 8(2):119-130.
[19] 李德仁. 我国第一颗民用三线阵立体测图卫星——资源三号测绘卫星[J]. 测绘学报, 2012, 41(3):317-322. LI Deren. China's First Civilian Three-line-array Stereo Mapping Satellite:ZY-3[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3):317-322.
[20] CHU Heng, CHEN Huagang, Zhu Weile. A New Remote Sensing Image Fusion Algorithm in the Decimated Wavelet Domain[J]. Opto-Electronic Engineering, 2009, 36(2):91-95.

融合图像的频率定量分析及其空间分辨率的确定方法

Quantitative Analysis in Frequency Domain and Determination of Spatial Resolution in Image Fusion

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