Bayesian Fusion of Multi-Band Images-Complementary results and supporting materials

In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques. In particular, low spatial resolution hyperspectral and multispectral images are fused to produce a high spatial resolution hyperspectral image.

[1]  Mathilde Bouriga,et al.  Estimation of covariance matrices based on hierarchical inverse-Wishart priors , 2011, 1106.3203.

[2]  Thomas S. Huang,et al.  Generative Bayesian Image Super Resolution With Natural Image Prior , 2012, IEEE Transactions on Image Processing.

[3]  John W. Paisley,et al.  Pan-sharpening with a Bayesian nonparametric dictionary learning model , 2014, AISTATS.

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

[5]  Russell C. Hardie,et al.  Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[7]  Jon Atli Benediktsson,et al.  SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[8]  Jing Wang,et al.  Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Jean-François Giovannelli,et al.  Sampling High-Dimensional Gaussian Distributions for General Linear Inverse Problems , 2012, IEEE Signal Processing Letters.

[10]  Yifan Zhang,et al.  Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[12]  J. Rosenthal,et al.  Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms , 2007, Journal of Applied Probability.

[13]  A. Gelman.  Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

[14]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Nikolas P. Galatsanos,et al.  Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images , 2006, IEEE Transactions on Image Processing.

[16]  Bin Wang,et al.  Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods , 2014, IEEE Geoscience and Remote Sensing Letters.

[17]  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.

[18]  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.

[19]  A. Kennedy,et al.  Hybrid Monte Carlo , 1988 .

[20]  Akira Iwasaki,et al.  Japanese hyper-multi spectral mission , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[21]  Manjunath V. Joshi,et al.  MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[22]  J. Campbell.  Introduction to remote sensing , 1987 .

[23]  Todd Wittman,et al.  A variational approach to hyperspectral image fusion , 2009, Defense + Commercial Sensing.

[24]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[25]  Naoto Yokoya,et al.  Hyperspectral and multispectral data fusion mission on hyperspectral imager suite (HISUI) , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[26]  Robert L. Stevenson,et al.  A Bayesian approach to image expansion for improved definitio , 1994, IEEE Trans. Image Process..

[27]  Russell C. Hardie,et al.  Application of the stochastic mixing model to hyperspectral resolution enhancement , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Xiao-Li Meng,et al.  Challenges and Advances in High Dimensional and High Complexity Monte Carlo Computation and Theory , 2012 .

[29]  Mohammad Alam,et al.  Superresolution Construction of Multispectral Imagery Based on Local Enhancement , 2008, IEEE Geoscience and Remote Sensing Letters.

[30]  Radford M. Neal.  Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[31]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[32]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[33]  Alfred O. Hero,et al.  Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery , 2009, IEEE Transactions on Signal Processing.

[34]  Radford M. Neal.  MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.

[35]  Jean-Yves Tourneret,et al.  Bayesian fusion of hyperspectral and multispectral images , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[36]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[37]  Guangyi Chen,et al.  Super-Resolution of Hyperspectral Imagery Using Complex Ridgelet Transform , 2012, Int. J. Wavelets Multiresolution Inf. Process..

[38]  Subhasis Chaudhuri,et al.  A Bayesian approach to visualization-oriented hyperspectral image fusion , 2013, Inf. Fusion.

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

[40]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[41]  E. M. Winter,et al.  Resolution enhancement of hyperspectral data , 2002, Proceedings, IEEE Aerospace Conference.

[42]  Jocelyn Chanussot,et al.  Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[43]  Mikhail F. Kanevski,et al.  Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images , 2008, IEEE Geoscience and Remote Sensing Letters.

[44]  Johannes R. Sveinsson,et al.  Model-Based Satellite Image Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Lorenzo Bruzzone,et al.  A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Shohreh Kasaei,et al.  Dimension Reduction of Optical Remote Sensing Images via Minimum Change Rate Deviation Method , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Aggelos K. Katsaggelos,et al.  A survey of classical methods and new trends in pansharpening of multispectral images , 2011, EURASIP J. Adv. Signal Process..

[48]  Yifan Zhang,et al.  A Bayesian Restoration Approach for Hyperspectral Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[49]  Jean-Yves Tourneret,et al.  Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Dehong Liu,et al.  Dictionary learning based pan-sharpening , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[51]  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.

[52]  Russell C. Hardie,et al.  MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor , 2004, IEEE Transactions on Image Processing.

[53]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[55]  Nebiye Musaoglu,et al.  Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis , 2009 .

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

[57]  Naoto Yokoya,et al.  Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Jean-Yves Tourneret,et al.  The Adaptive Coherence Estimator is the Generalized Likelihood Ratio Test for a Class of Heterogeneous Environments , 2008, IEEE Signal Processing Letters.

[59]  Christophe Andrieu,et al.  Bayesian curve fitting using MCMC with applications to signal segmentation , 2002, IEEE Trans. Signal Process..