Two improvement schemes of PAN modulation fusion methods for spectral distortion minimization

Fusion of panchromatic (PAN) and multispectral (MS) images is one of the most promising issues in remote sensing. PAN modulation fusion methods are usually based on an assumption that a ratio of two different‐resolution versions of an MS band is equal to a ratio of two different‐resolution versions of a PAN image. In such fusion methods, image haze is rarely taken into account, and it may produce serious spectral distortion in synthetic images. In this paper, assuming that the previous ratio relationship only holds for haze‐free images, two relevant improvement schemes are proposed to better express the ratio relationship of haze‐included images. In a test on a spatially degraded IKONOS dataset, the first scheme synthesizes an image with minimum spectral distortion, and the second modifies several current PAN modulation fusion methods and generates high‐quality synthetic products. The experiment results confirm that image haze can seriously impact the quality of fused images obtained by using PAN modulation fusion methods, and it should be taken into account in relevant image fusion.

[1]  J. Chassery,et al.  The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multisp , 1996 .

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

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

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

[5]  Yun Zhang.  PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS , 2002 .

[6]  Luciano Alparone,et al.  Remote sensing image fusion using the curvelet transform , 2007, Inf. Fusion.

[7]  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..

[8]  W. A. Hallada,et al.  Image sharpening for mixed spatial and spectral resolution satellite systems , 1983 .

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

[10]  M. S. Moran,et al.  Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output , 1992 .

[11]  M. S. Moran,et al.  A window-based technique for combining landsat thematic mapper thermal data with higher-resolution multispectral data over agricultural lands , 1990 .

[12]  Costas A. Varotsos,et al.  Power‐law correlations in column ozone over Antarctica , 2005 .

[13]  Yuri Zhang,et al.  A new automatic approach for effectively fusing Landsat 7 as well as IKONOS images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[14]  J. G. Liu,et al.  Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details , 2000 .

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

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

[17]  J. Schott,et al.  Resolution enhancement of multispectral image data to improve classification accuracy , 1993 .

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

[19]  A. Cracknell,et al.  Long‐range persistence in global Aerosol Index dynamics , 2006 .

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

[21]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[22]  Robert A. Schowengerdt,et al.  Reconstruction of multispatial, multispectral image data using spatial frequency content , 1980 .

[23]  Costas Varotsos,et al.  Modern Computational Techniques for Environmental Data; Application to the Global Ozone Layer , 2005, International Conference on Computational Science.

[24]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[25]  Philip N. Slater,et al.  Earth remote sensing using the landsat thematic mapper and spot sensor systems : 15-17 April 1986, Innsbruck, Austria , 1986 .

[26]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[27]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[28]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[29]  Costas A. Varotsos,et al.  Scaling properties of air pollution in Athens, Greece and Baltimore, Maryland , 2005 .

[30]  Costas A. Varotsos,et al.  Technical Note: Long-term memory effect in the atmospheric CO 2 concentration at Mauna Loa , 2006 .

[31]  P. Chavez.  Digital merging of Landsat TM and digitized NHAP data for 1: 24,000-scale image mapping((National Hi , 1986 .

[32]  Luciano Alparone,et al.  A global quality measurement of pan-sharpened multispectral imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[33]  Mario Lillo-Saavedra,et al.  Multispectral images fusion by a joint multidirectional and multiresolution representation , 2007 .

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

[35]  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.

[36]  Costas A. Varotsos,et al.  under a Creative Commons License. Atmospheric Chemistry and Physics Long-memory processes in ozone and temperature variations at the , 2006 .

[37]  Sergio Teggi,et al.  TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm , 2003 .

[38]  Thierry Ranchin,et al.  The wavelet transform for the analysis of remotely sensed images , 1993 .

[39]  J. Boardman,et al.  Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm , 1992 .