Best tradeoff for remote sensing image fusion based on three-dimensional variation and à trous wavelet

Abstract For à trous wavelet-based remote sensing image fusion, it is significant to determine the number of decomposition levels (NDL) to derive the best tradeoff between the spatial and spectral qualities of the fused image. The tradeoff is modeled as a cost function from a novel three-dimensional variational point of view. By solving this cost function, a new fusion scheme is proposed that can be used to customize the tradeoff by weighting the wavelet planes with a factor. The optimal NDL is decided by the intersection of the characteristic curves of the spatial and spectral qualities. Three pairs of remote sensing images are used to test the analysis. The experimental results show that the proposed method provides the practitioner with a reference for selecting the fused image with different spatial and spectral qualities for different applications.

[1]  Luciano Alparone,et al.  Landsat ETM+ and SAR image fusion based on generalized intensity Modulation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[3]  Jelena Kovacevic,et al.  Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for Rn , 1992, IEEE Trans. Inf. Theory.

[4]  Lucien Wald,et al.  Quality of high resolution synthesised images: Is there a simple criterion ? , 2000 .

[5]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[6]  Lincheng Shen,et al.  Optimizing the number of decomposition levels for wavelet-based multifocus image fusion , 2010, 2010 8th World Congress on Intelligent Control and Automation.

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

[8]  Gonzalo Pajares,et al.  A wavelet-based image fusion tutorial , 2004, Pattern Recognit..

[9]  Renhua Zhang,et al.  Fusing remote sensing images using à trous wavelet transform and empirical mode decomposition , 2008, Pattern Recognit. Lett..

[10]  M. Trippenbach,et al.  A hybrid variational method of describing pulse splitting by dispersion management , 2006 .

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

[13]  Wen Dou,et al.  A general framework for component substitution image fusion: An implementation using the fast image fusion method , 2007, Comput. Geosci..

[14]  R. Li,et al.  Level Selection for Multiscale Fusion of Out-of-Focus Image , 2005, IEEE Signal Process. Lett..

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

[16]  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.

[17]  Roger L. King,et al.  Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Xavier Otazu,et al.  Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Michael I. Jordan,et al.  Bayesian parameter estimation via variational methods , 2000, Stat. Comput..

[20]  P. S. Chavez,et al.  Comparison of the spectral information content of Landsat Thematic Mapper and SPOT for three different sites in the Phoenix, Arizona region , 1988 .

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

[22]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  P. Dutilleux.  An Implementation of the “algorithme à trous” to Compute the Wavelet Transform , 1989 .

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