全色-多光谱影像融合技术可以显著提高遥感影像的地物判别能力，但是空间信息融入度与光谱信息保真度是相互矛盾的一组性质，一般方法往往无法平衡这两方面。SFIM算法具有良好的光谱信息保持能力，但是其空间信息融入度较差，影响了整体的融合效果。为此，本文分析了SFIM模型的原理与特点，提出一种自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法（AGSFIM）。以均值调整后的多光谱整体平均梯度为标准来计算高斯滤波的最优参数，将下采样全色影像的清晰度调整至同样水平，以保证融合结果的空间信息融入度与光谱信息保真度之间的平衡。利用6种融合算法对“北京二号”（Beijing-2）、“资源三号”02星（ZY-3 02）数据进行对比试验，表明在良好的光谱保持能力的前提下，改进方法可以有效克服SFIM算法空间信息融入不足的缺点。
Panchromatic and multi-spectral fusion technology can increase feature discriminant ability of remote sensing images.However,the abilities of fusing spatial information and keeping spectral information are conflict,and are hard to be balanced by common algorithms.SFIM (smoothing filter-based intensity modulation) can keep spectral information effectively,but is difficult to fuse spatial information which will reduces the holistic effect.Pointing to this problem,this paper analyzes the principles and characters of SFIM model,and proposes a fusion method combined with adaptive Gaussian filter and SFIM model (AGSFIM).Computing optimal parameter of Gaussian filter based on entirety mean-value-adjusted average gradient of multi-spectral bands,and adjusting down-sampled panchromatic image to same sharpness level which can confirm the balances of spatial information fusing ability and spectral information keeping ability.Beijing-2 and ZY-3 02 data are applied to test and six different fusion methods are used to compare.The experiments show that AGSFIM can effectively overcome SFIM's shortage and increase fusion images' spatial information.
[1] 李春华, 徐涵秋. 高分辨率遥感图像融合的光谱保真问题[J]. 地球信息科学, 2008, 10(4): 520-526. LI Chunhua, XU Hanqiu. Spectral Fidelity in High-resolution Remote Sensing Image Fusion[J]. Geo-Information Science, 2008, 10(4): 520-526.
[2] LIU J G. Smoothing Filter-based Intensity Modulation: A Spectral Preserve Image Fusion Technique for Improving Spatial Details[J]. International Journal of Remote Sensing, 2000, 21(18): 3461-3472.
[3] 何贵青, 齐敏, 赵海涛, 等. 一种基于SFIM和IHS变换的图像融合算法[J]. 西北工业大学学报, 2008, 26(1): 41-46. HE Guiqing, QI Min, ZHAO Haitao, et al. A New and Better Image Fusion Algorithm based on SFIM Model and IHS Transform[J]. Journal of Northwestern Polytechnical University, 2008, 26(1): 41-46.
[4] ZHANG Yun. Problems in the Fusion of Commercial High-Resolution Satellites Images as well as LANDSAT 7 Images and Initial Solutions[C]//Proceedings of the International Archives of Photogrammetry and Remote Sensing. Ottawa, 2002: 9-12.
[5] ZHANG Yuri. A New Automatic Approach for Effectively Fusing Landsat 7 as well as IKONOS Images[C]//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Toronto, Ontario, Canada: IEEE, 2002: 2429-2431.
[6] VRABEL J. Multispectral Imagery Advanced Band Sharpening Study[J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(1): 73-80.
[7] HAYDN R, DALKE G W, HENKEL J, et al. Application of the IHS Color Transform to the Processing of Multisensor Data and Image Enhancement[C]//Proceedings of the International Symposium on Remote Sensing of Environment, First Thematic Conference: Remote Sensing of Arid and Semi-Arid Lands. Ann Arbor MI: Environmental Research Institute of Michigan, 1982.
[8] 楚恒, 王汝言, 朱维乐. DCT域遥感影像融合算法[J]. 测绘学报, 2008, 37(1): 70-76. DOI: 10.3321/j.issn:1001-1595.2008.01.013. CHU Heng, WANG Ruyan, ZHU Weile. Remote Sensing Image Fusion Algorithm in the DCT Domain[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(1): 70-76. DOI: 10.3321/j.issn:1001-1595.2008.01.013.
[9] AIAZZI B, BARONTI S, SELVA M, et al. Enhanced Gram-Schmidt Spectral Sharpening Based on Multivariate Regression of MS and Pan Data[C]//IEEE International Symposium on Geoscience and Remote Sensing. Denver, CO, USA: IEEE, 2006: 3806-3809.
[10] 张涛, 刘军, 杨可明, 等. 结合Gram-Schmidt变换的高光谱影像谐波分析融合算法[J]. 测绘学报, 2015, 44(9): 1042-1047. DOI: 10.11947/j.AGCS.2015.20140637. ZHANG Tao, LIU Jun, YANG Keming, et al. Fusion Algorithm for Hyperspectral Remote Sensing Image Combined with Harmonic Analysis and Gram-Schmidt Transform[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9): 1042-1047. DOI: 10.11947/j.AGCS.2015.20140637.
[11] SHETTIGARA V K. A Generalized Component Substitution Technique for Spatial Enhancement of Multispectral Images Using A Higher Resolution Data Set[J]. Photogrammetric Engineering and Remote Sensing, 1992, 58(5): 561-567.
[12] SCHOWENGERDT R A. Reconstruction of Multispatial, Multispectral Image Data Using Spatial Frequency Content[J]. Photogrammetric Engineering and Remote Sensing, 1980, 46(10): 1325-1334.
[13] MALLAT S G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[14] ZHANG Yun, HONG Gang. An IHS and Wavelet Integrated Approach to Improve Pan-Sharpening Visual Quality of Natural Colour IKONOS and QuickBird Images[J]. Information Fusion, 2005, 6(3): 225-234.
[15] POHL C, VAN GENDEREN J L. Review Article Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications[J]. International Journal of Remote Sensing, 1998, 19(5): 823-854.
[16] 杨景辉. 遥感影像像素级融合通用模型及其并行计算方法[D]. 武汉: 武汉大学, 2014. YANG Jinghui. The Generalized Model and Parallel Computing Methods for Pixel-Level Remote Sensing Image Fusion[J]. Wuhan: Wuhan University, 2014.
[17] 韩冰, 赵银娣. 一种改进的SFIM高光谱图像融合算法[J]. 遥感信息, 2012, 27(5): 44-47, 54. HAN Bing, ZHAO Yindi. An Improved Smoothing Filter-based Intensity Modulation Algorithm for Hyperspectral Image Fusion[J]. Remote Sensing Information, 2012, 27(5): 44-47, 54.
[18] ELACHI C, ZIMMERMAN P D. Introduction to the Physics and Techniques of Remote Sensing[J]. Physics Today, 1988, 41(11): 126-126.
[19] GUO Liujian, MOORE J M, HAIGH J D. Simulated Reflectance Technique for ATM Image Enhancement[J]. International Journal of Remote Sensing, 1997, 18(2): 243-254.
[20] 李艳雯, 杨英宝, 程三胜. 基于亮度平滑滤波调节(SFIM)的SPOT5影像融合[J]. 遥感信息, 2007(1): 63-66. LI Yanwen, YANG Yingbao, CHENG Sansheng. The SPOT-5 Image Fusion by Smoothing Filter-based Intensity Modulation (SFIM)[J]. Remote Sensing Information, 2007(1): 63-66.
[21] 陈超, 江涛, 徐文学. 基于平均梯度的多聚焦图像融合方法研究[J]. 山东建筑大学学报, 2009, 24(2): 133-137. CHEN Chao, JIANG Tao, XU Wenxue. Research on Multi-Focus Image Fusion Methods based on Mean Gradient[J]. Journal of Shandong Jianzhu University, 2009, 24(2): 133-137.
[22] 赵鲁燕, 尹君. 图像融合效果评价方法研究[J]. 遥感信息, 2005(4): 16-17. ZHAO Luyan, YIN Jun. Study on the Effect Evaluation of Image Fusion[J]. Remote Sensing Information, 2005(4): 16-17.
[23] 苏媛媛, 李英杰, 周志峰. 遥感图像融合算法与质量评价探讨[J]. 工程勘察, 2012, 40(12): 70-74. SU Yuanyuan, LI Yingjie, ZHOU Zhifeng. Remote Sensing Image Fusion Methods and Their Quality Evaluation[J]. Geotechnical Investigation & Surveying, 2012, 40(12): 70-74.
[24] SHANNON C E. A Mathematical Theory of Communication[J]. ACM SIGMOBILE Mobile Computing and Communications Review, 2001, 5(1): 3-55.
[25] 贠培东, 曾永年, 历华. 不同遥感影像融合方法效果的定量评价研究[J]. 遥感信息, 2007(4): 40-45. YUN Peidong, ZENG Yongnian, LI Hua. Quantitative Assessment of Image Fusion Methods[J]. Remote Sensing Information, 2007(4): 40-45.
