Combining Component Substitution and Multiresolution Analysis: A Novel Generalized BDSD Pansharpening Algorithm

Modern optical satellites can acquire bundles of panchSromatic (PAN) and multispectral (MS) images of the scene simultaneously. Because of the complexity of the sensors and amount of data involved, an MS image always has lower spatial resolution than the corresponding PAN image. Pansharpening aims at fusing MS images and PAN images, characterized by the spectral content of the former and the spatial details of the latter. There are two main large families of pansharpening algorithms, i.e., component substitution (CS) and multiresolution analysis (MRA). Generally speaking, the CS algorithms have better performance on spatial detail injection, while the MRA shows better spectral content preservation. In this paper, we propose a novel pansharpening algorithm, which combines the conceptions of CS and MRA. This proposed algorithm can be regarded as a generalized version of the existing band-dependent spatial-detail (BDSD) algorithm. A semisimulated dataset and three real datasets are adopted to compare the performance among the generalized-BDSD algorithm and six existing popular pansharpening algorithms. It shows that the proposed method has much lower spectral distortion and good visual appearance. In other words, the proposed method aggregates the advantages of CS and MRA, which shows effectiveness in practice.

[1]  Johannes R. Sveinsson,et al.  MTF-Based Deblurring Using a Wiener Filter for CS and MRA Pansharpening Methods , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Jocelyn Chanussot,et al.  Context-Adaptive Pansharpening Based on Image Segmentation , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Mehmet Celenk,et al.  Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Luciano Alparone,et al.  Spatial Methods for Multispectral Pansharpening: Multiresolution Analysis Demystified , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[8]  Henry Leung,et al.  Covariance intersection based image fusion technique with application to pansharpening in remote sensing , 2010, Inf. Sci..

[9]  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.

[10]  Lei Cao,et al.  A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation , 2014 .

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

[12]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[13]  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.

[14]  Licheng Jiao,et al.  Multiple Kernel Learning Based on Discriminative Kernel Clustering for Hyperspectral Band Selection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[18]  Pratibha P. Shingare,et al.  Fusion classification of multispectral and panchromatic image using improved decision tree algorithm , 2014, 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014).

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

[20]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

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

[22]  Shutao Li,et al.  Image Fusion Using Nonsubsampled Contourlet Transform , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[23]  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.

[24]  Luciano Alparone,et al.  A Laplacian pyramid with rational scale factor for multisensor image data fusion , 1997 .

[25]  Thierry Ranchin,et al.  Liu 'Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details' , 2002 .

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

[27]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[28]  B. Luo,et al.  Large-Scale Graph Database Indexing Based on T-mixture Model and ICA , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

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