Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amelioration de la Resolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments.

[1]  Johannes R. Sveinsson,et al.  Spectral consistent satellite image fusion: using a high resolution panchromatic and low resolution multi-spectral images , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[2]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Thierry Ranchin,et al.  Importance and effect of co-registration quality in an example of “ pixel to pixel ” fusion process , 1998 .

[4]  Xavier Otazu,et al.  A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  J. Zhou,et al.  A wavelet transform method to merge Landsat TM and SPOT panchromatic data , 1998 .

[6]  D. Yocky Multiresolution wavelet decomposition image merger of landsat thematic mapper and SPOT panchromatic data , 1996 .

[7]  Lucien Wald,et al.  Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions , 2002 .

[8]  Luciano Alparone,et al.  Image fusion—the ARSIS concept and some successful implementation schemes , 2003 .

[9]  Gemma Piella,et al.  A general framework for multiresolution image fusion: from pixels to regions , 2003, Inf. Fusion.

[10]  José A. Malpica,et al.  Hue Adjustment to IHS Pan-Sharpened IKONOS Imagery for Vegetation Enhancement , 2007, IEEE Geoscience and Remote Sensing Letters.

[11]  Myungjin Choi,et al.  A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter , 2006, IEEE Trans. Geosci. Remote. Sens..

[12]  J. E. Bare,et al.  Application of the IHS color transform to the processing of multisensor data and image enhancement , 1982 .

[13]  E. Csaplovics,et al.  Examination of image fusion using synthetic variable ratio (SVR) technique , 2007 .

[14]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[15]  D Pradines Improving Spot Images Size And Multispectral Resolution , 1986, Other Conferences.

[16]  C. Thomas Fusion d'images de résolutions spatiales différentes , 2006 .

[17]  Vassilia Karathanassi,et al.  Investigation of the Dual-Tree Complex and Shift-Invariant Discrete Wavelet Transforms on Quickbird Image Fusion , 2007, IEEE Geoscience and Remote Sensing Letters.

[18]  T. Tu,et al.  A target fusion-based approach for classifying high spatial resolution imagery , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[19]  Jun Li SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2010 .

[20]  J. C. Price,et al.  Combining multispectral data of differing spatial resolution , 1999, IEEE Trans. Geosci. Remote. Sens..

[21]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[22]  J. M. Moore,et al.  Pixel block intensity modulation: Adding spatial detail to TM band 6 thermal imagery , 1998 .

[23]  Sophia Antipolis,et al.  Assessment of the quality of fused products , 2004 .

[24]  S. Sides,et al.  Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic , 1991 .

[25]  J. G. Liu,et al.  Smoothing Filter-based Intensity Modulation : a spectral preserve image fusion technique for improving spatial details , 2001 .

[26]  A. H. J. M. Pellemans,et al.  MERGING MULTISPECTRAL AND PANCHROMATIC SPOT IMAGES WITH RESPECT TO THE RADIOMETRIC PROPERTIES OF THE SENSOR , 1993 .

[27]  Zhenhua Li,et al.  Color transfer based remote sensing image fusion using non-separable wavelet frame transform , 2005, Pattern Recognit. Lett..

[28]  Mario Lillo-Saavedra,et al.  Spectral or spatial quality for fused satellite imagery? A trade‐off solution using the wavelet à trous algorithm , 2006 .

[29]  Jocelyn Chanussot,et al.  Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Thierry Blu,et al.  Using iterated rational filter banks within the ARSIS concept for producing 10 m Landsat multispectral images , 1998 .

[31]  Lucien Wald,et al.  A MTF-Based Distance for the Assessment of Geometrical Quality of Fused Products , 2006, 2006 9th International Conference on Information Fusion.

[32]  Jun Li SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2000 .

[33]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[34]  Rafael García,et al.  Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geosci. Remote. Sens. Lett..

[36]  V. K. Shettigara,et al.  A generalized component substitution technique for spatial enhancement of multispectral images using , 1992 .

[37]  Y. Chibani,et al.  The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images , 2002 .

[38]  Laura Igual,et al.  A Variational Model for P+XS Image Fusion , 2006, International Journal of Computer Vision.

[39]  Nicolas H. Younan,et al.  Quality analysis of pansharpened images , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[40]  Te-Ming Tu,et al.  A new look at IHS-like image fusion methods , 2001, Inf. Fusion.

[41]  Y. Zhang,et al.  A new merging method and its spectral and spatial effects , 1999 .

[42]  Xiaokang Yang,et al.  Fusion of multispectral and panchromatic satellite images based on contourlet transform and local average gradient , 2007 .

[43]  Te-Ming Tu,et al.  Best Tradeoff for High-Resolution Image Fusion to Preserve Spatial Details and Minimize Color Distortion , 2007, IEEE Geoscience and Remote Sensing Letters.

[44]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[45]  Alan R. Gillespie,et al.  Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques , 1987 .

[46]  Jordi Inglada,et al.  Analysis of Artifacts in Subpixel Remote Sensing Image Registration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Te-Ming Tu,et al.  A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[48]  W. Shi,et al.  Wavelet-based image fusion and quality assessment , 2005 .

[49]  L. Alparone,et al.  An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas , 2003, 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.

[50]  Arthur Filippidis,et al.  Multisensor data fusion for surface land-mine detection , 2000, IEEE Trans. Syst. Man Cybern. Part C.