Adaptive remote-sensing image fusion based on dynamic gradient sparse and average gradient difference

ABSTRACT A new method for remote-sensing image fusion based on variational methods and the image objective evaluation model is proposed. Different from the previous methods, the proposed method does not make big improvement on the variational model but focuses on how to make the calculation of existing method more accurate. The problem is that in the solving process of some variational models, it cannot be determined by the information of input images to gain the accurate calculation results. To solve this problem, a new model based on the average gradient of the objective evaluation index is proposed. The measured value of the proposed model is used in the iterations of the fusion algorithm as a feedback to adaptively adjust the algorithm to improve the quality of the fused results. Experiments show that the proposed adaptive method significantly improves the spatial information and well preserves the spectral information in the view of the subjective and objective evaluations.

[1]  Delu Zeng,et al.  Pan-Sharpening with a Hyper-Laplacian Penalty , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[3]  Pengfei Liu,et al.  Spatial-Hessian-Feature-Guided Variational Model for Pan-Sharpening , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[4]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

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

[6]  J. R. Raol,et al.  Pixel-level Image Fusion using Wavelets and Principal Component Analysis , 2008 .

[7]  Chaomin Shen,et al.  Adaptive regularized scheme for remote sensing image fusion , 2016, Frontiers of Earth Science.

[8]  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.

[9]  S. Ashraf,et al.  Image data fusion for the remote sensing of freshwater environments , 2012 .

[10]  Myungjin Choi,et al.  A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter , 2006, IEEE Trans. Geosci. Remote. Sens..

[11]  Xiao-Hui Yang,et al.  Fusion Algorithm for Remote Sensing Images Based on Nonsubsampled Contourlet Transform , 2008 .

[12]  R. E. Walker,et al.  Color enhancement of highly correlated images. I - Decorrelation and HSI contrast stretches. [hue saturation intensity , 1986 .

[13]  Haixu Wang,et al.  Multimodal medical image fusion based on IHS and PCA , 2010 .

[14]  Michael Möller,et al.  An Adaptive IHS Pan-Sharpening Method , 2010, IEEE Geoscience and Remote Sensing Letters.

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

[16]  Liangpei Zhang,et al.  A Remote Sensing Image Fusion Method Based on the Analysis Sparse Model , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Shutao Li,et al.  Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries , 2013, 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]  Wenzhong Shi,et al.  Multisource Image Fusion Method Using Support Value Transform , 2007, IEEE Transactions on Image Processing.

[20]  A. Kallel,et al.  Iterative scheme for MS image pansharpening based on the combination of multi-resolution decompositions , 2016 .

[21]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[22]  Laura Igual,et al.  A Variational Model for P+XS Image Fusion , 2006, International Journal of Computer Vision.

[23]  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.

[24]  Mark W. Schmidt,et al.  Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization , 2011, NIPS.

[25]  Yu Liu,et al.  A general framework for image fusion based on multi-scale transform and sparse representation , 2015, Inf. Fusion.

[26]  Wei Liu,et al.  Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[28]  Lorenzo Bruzzone,et al.  Image fusion techniques for remote sensing applications , 2002, Inf. Fusion.

[29]  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.

[30]  John van Genderen,et al.  Structuring contemporary remote sensing image fusion , 2015 .

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

[32]  Michael Möller,et al.  A Variational Approach for Sharpening High Dimensional Images , 2012, SIAM J. Imaging Sci..