遙感探測利用衛星或載具之光學機械掃描系統來偵測地面物體之各種不同波長之自然電磁波，或以系統本身主動發射電磁波再由接收器偵測反射回來之電磁波，從空中獲取大範圍涵蓋面積之地面資訊，然而，一個感測器要能同時提供具備高空間解析力及高光譜解析力的影像卻在技術上有相當的困難，因此，影像融合(Image Fusion)技術可以結合來自於不同感測器的影像，並擷取各自影像中的優點以融合成兼具融合前各影像優點之新影像，以融合一張高解析力全色態影像和一張低解析力多光譜影像而言，此融合影像應兼具全色態影像中的高解析力又具有多光譜影像的彩色資訊。然而，影像融合技術雖能使高解析力全色態影像與低解析力多光譜影像進行融合得到具高解析力的多光譜影像，但是並不表示融合技術就能融合任何解析力差異倍數的全色態影像與多光譜影像，因為影像融合技術的前提即在於能同時保有各影像融合前之優點，因此，為了評估全色態影像和多光譜影像融合時解析力差異倍數之限制，本研究以各個不同解析力差異倍數的高解析力全色態影像與低解析力多光譜影像，應用IHS影像融合法與小波函數影像融合法進行影像融合，並且為了評估融合影像是否保留住高解析力全色態影像的空間資訊與低解析力多光譜影像的多光譜資訊，分別就影像的光譜資訊和紋理兩方面以統計的方法卡方檢定進行評估其融合結果，探討其融合時差異倍數限制。

Satellites can detect various wavelengths from ground in remote sensing, but it is difficult technologically that single sensor can not provides both high spectrum and resolution image. Therefore, image fusion is an useful technique to integrate images from different sensors and produce a new fused image, and the fused image has the advantages of every images. For instance, the new image which is fused from the high resolution panchromatic image and the low resolution multispectrum image must contain high resolution from the panchromatic image and color information from the multispectrum image. Although we can fuse a high resolution panchromatic image and a low resolution multispectrum image to get a fusion image with high resolution and multispectrum, it doesn’t represent image fusion can fuse panchromatic image and multispectrum image at any resolution differences. It is necessary to keep advantages of high resolution from the panchromatic image and color information from the multispectrum image. Therefore, in order to evaluate the limit of resolution difference between panchromatic image and multispectrum image, our method is to fuse high resolution panchromatic image and low resolution multispectrum image in every different resolution differences with IHS image fusion and wavelet image fusion. Then, using Chi-Square test from spectrum and texture evaluates whether fusion image keeps spacial information from the panchromatic image and color information from the multispectrum image.

江中熙，”衛星影像融合技術之評估與精進”，碩士論文，國防大學中正理工學院，2000
唐德誠，” 灰度共現矩陣於多波段多極化SAR影像分類之研究”，碩士論文，國防大學中正理工學院，2003
許丕政，”應用統計檢定法於衛星影像變遷偵測之研究”，碩士論文， 國立中央大學土木工程研究所，1998
莊家和，” 紋路特徵編碼法於紋路分析之研究”，碩士論文， 南華大學，1998
張紘炬，”統計學”，華泰書局，2002
葉兆輝，”統計學導論”，台灣西書出版社，2001
鄭心惠，” 遙測影像空間品質之評估”，碩士論文，國立成功大學測量工程研究所，2003
Carper, W. J., Lillesand, T. M., and Kiefer, R. W., “The Use of Intensity-Hue-Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data,” Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 4, p p.459-467, 1990
Chavez, P. S. and Kwarteng, A. Y., “Extracting Spectral Contrast in Landsat Thematic Mapper Image Data Using Selective Principal Component Analysis,” Photogrammetric Engineering and Remote Sensing, Vol. 55, No. 3, pp.339-348, 1989
Chavez, Jr. P. S., Sides, S. C., and Anderson, J. A., “Comparison of Three Difference Methods to Merge Multiresolution and Multispectral Data: Landsat TM and SPOT Panchromatic,” Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 3 pp.295-303, 1991
Daubechies, I., “Time-frequency localization operators:a geometric phase space approach,” IEEE Trans. Inform. Theory, 34, pp. 605-612, 1988
Daubechies, I.,”Ten Lectures on Wavelets”,Printed by Capital City Press, Montpelier,Vermont,1992
Duport, B. G., Girel, J., Chassery, J. M., and Pautou, G., “The Use of Multiresolution Analysis and Wavelets Transform for Merging SPOT Panchromatic and Multispectral Image Data,” Photogrammetric Engineering and Remote Sensing, Vol. 62, No. 9, pp.1057-1066, 1996
Gonzalez, R. C. and Woods, R. E., “Digital Image Processing”, Addison Wessley Publishing Company,1992
Haar, “Zur Throrie der orthogonalen Funktionen-Systeme,” Math. Ann., 69, pp.331-371, 1910
Haralick, R. M., “Statistical and structural approaches to texture,” Proc. IEEE, Vol. 67, pp.784-804, 1979
Haralick, R. M., Shanmugam, K., and Dinstein, ITS’HAK, “Texture Feature for Image Classification,” IEEE Trans. On Systems, Man,and Cybernetics, Vol. Smc-3, No. 6, pp. 610-621, 1973.
Jorge, N., Xavier, O., Octavi, F., and Albert, P., “Simultaneous Image Fusion and Reconstruction Using Wavelets Application to SPOT+LANDSAT Images,” Vistas in Astronomy, Vol. 41, No. 3, pp.351-357, 1997
Jorge, N., Xavier, O., Octavi, F., Albert, P., Vicenc, P., and Roman, A., “Multiresolution-Based Imaged Fusion with Additive Wavelet Decomposition,” IEEE Trans. on Geoscience and Remote sensing, Vol. 37, No. 3, pp.1204-1211, 1999
Mallat, S., “A Theory for Multiresolution Signal: The Wavelet Representation,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, July 1989
Morlet, J., Arens, G., Fourgeau, I. and Giard D., “Wave Propagation and Sampling Theory,”Geophysics, 47, pp. 203-236, 1982
Park J. H., “Multisensor data fusion using wavelet transform,” IECI Chapter Japan Series Vol. 3 No. 1, 2001
Pohl, C. and Van, J. L., Genderen, “Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications,” Int. J. Remote Sensing, Vol. 19, No. 5, pp.823-854, 1998
Treitz, P. M., Filho, O. R., Howarth, P. J., and Soulis, E. D., “Textural Processing of Multi-Polarization SAR for Agricultural Crop Classifition,” International Geoscience and Remote Sensing Symposium, pp. 1986-1988, 1996
Zhou, J., Civco, D. L., and Silander, J. A., “A wavelet transform method to merge Landsat TM and SPOT panchromatic data,” Int. J. Remote Sensing, vol. 19, no. 4, pp. 743–757, 1998