Full Scale Regression-Based Injection Coefficients for Panchromatic Sharpening

Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.

[1]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[2]  P. Chavez,et al.  Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis , 1989 .

[3]  Jocelyn Chanussot,et al.  Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept , 2008, IEEE Geoscience and Remote Sensing Letters.

[4]  Andrea Garzelli,et al.  Pansharpening of Multispectral Images Based on Nonlocal Parameter Optimization , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Francisca López-Granados,et al.  Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery , 2009 .

[6]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

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

[8]  D. Roberts,et al.  Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models , 2003 .

[9]  Hassan Ghassemian,et al.  Nonlinear IHS: A Promising Method for Pan-Sharpening , 2016, IEEE Geoscience and Remote Sensing Letters.

[10]  Jocelyn Chanussot,et al.  Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  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.

[12]  Gaurav Sharma,et al.  A Regularized Model-Based Optimization Framework for Pan-Sharpening , 2014, IEEE Transactions on Image Processing.

[13]  Hassan Ghassemian,et al.  A review of remote sensing image fusion methods , 2016, Inf. Fusion.

[14]  Jocelyn Chanussot,et al.  Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics , 2008, IEEE Transactions on Geoscience and Remote Sensing.

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

[16]  Luciano Alparone,et al.  MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery , 2006 .

[17]  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.

[18]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Roger L. King,et al.  An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Bruno Aiazzi,et al.  A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

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

[22]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

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

[24]  Luciano Alparone,et al.  Full scale assessment of pansharpening methods and data products , 2014, Remote Sensing.

[25]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[26]  Shutao Li,et al.  Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[27]  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..

[28]  A. Mohammadzadeh,et al.  Road extraction based on fuzzy logic and mathematical morphology from pan‐sharpened ikonos images , 2006 .

[29]  Guy Flouzat,et al.  Data fusion thanks to an improved morphological pyramid approach: comparison loop on simulated images and application to SPOT 4 data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[30]  Wei Liu,et al.  SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework , 2015, IEEE Transactions on Image Processing.

[31]  Shutao Li,et al.  Pixel-level image fusion: A survey of the state of the art , 2017, Inf. Fusion.

[32]  Jocelyn Chanussot,et al.  A Pansharpening Method Based on the Sparse Representation of Injected Details , 2015, IEEE Geoscience and Remote Sensing Letters.

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

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

[35]  Richard Bamler,et al.  A Sparse Image Fusion Algorithm With Application to Pan-Sharpening , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[36]  W. J. Carper,et al.  The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data , 1990 .

[37]  Jocelyn Chanussot,et al.  A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors , 2014, IEEE Transactions on Image Processing.

[38]  Andrea Garzelli,et al.  Hypercomplex Quality Assessment of Multi/Hyperspectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[39]  Luciano Alparone,et al.  A Theoretical Analysis of the Effects of Aliasing and Misregistration on Pansharpened Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.

[40]  Luciano Alparone,et al.  Intersensor Statistical Matching for Pansharpening: Theoretical Issues and Practical Solutions , 2017, IEEE Transactions on Geoscience and Remote Sensing.

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

[42]  Jocelyn Chanussot,et al.  Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening , 2014, IEEE Geoscience and Remote Sensing Letters.

[43]  Shutao Li,et al.  A New Pan-Sharpening Method Using a Compressed Sensing Technique , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Bruno Aiazzi,et al.  Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Francesca Bovolo,et al.  Analysis of the Effects of Pansharpening in Change Detection on VHR Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[46]  Fang Li,et al.  A Variational Approach for Pan-Sharpening , 2013, IEEE Transactions on Image Processing.

[47]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[48]  Johannes R. Sveinsson,et al.  A New Pansharpening Algorithm Based on Total Variation , 2014, IEEE Geoscience and Remote Sensing Letters.

[49]  Jocelyn Chanussot,et al.  Fusion of Multispectral and Panchromatic Images Based on Morphological Operators , 2016, IEEE Transactions on Image Processing.

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

[51]  Andrea Garzelli,et al.  Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[52]  T. Ranchin,et al.  Liu 'Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details' , 2002 .

[53]  S. Baronti,et al.  Twenty-Five Years of Pansharpening: A Critical Review and New Developments , 2012 .

[54]  Davide Cozzolino,et al.  Pansharpening by Convolutional Neural Networks , 2016, Remote. Sens..

[55]  Jocelyn Chanussot,et al.  Pansharpening Based on Semiblind Deconvolution , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[57]  Jocelyn Chanussot,et al.  A Pansharpening Algorithm Based on Morphological Filters , 2015, ISMM.

[58]  Kiyun Yu,et al.  A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement , 2011, IEEE Transactions on Geoscience and Remote Sensing.