Application of Morphological Component Analysis to Optical Image Fusion

The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information com pare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%.

[1]  Yun Zhang,et al.  Wavelet based image fusion techniques — An introduction, review and comparison , 2007 .

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

[3]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[4]  Lorenzo Bruzzone,et al.  A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

[6]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[7]  Nicola Asuni,et al.  Fast Artifacts-Free Image Interpolation , 2008, BMVC.

[8]  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.

[9]  Qiang Zheng,et al.  Remote sensing image fusion method based on multiscale morphological component analysis , 2016 .

[10]  Elias Bareinboim,et al.  Causal inference and the data-fusion problem , 2016, Proceedings of the National Academy of Sciences.

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

[12]  Boualem Boashash,et al.  Image fusion-based contrast enhancement , 2012, EURASIP Journal on Image and Video Processing.

[13]  S. Baronti,et al.  Multispectral and panchromatic data fusion assessment without reference , 2008 .

[14]  Senlin Yan,et al.  Bivariate Hahn moments for image reconstruction , 2014, Int. J. Appl. Math. Comput. Sci..

[15]  Mehran Yazdi,et al.  Improved IHS Pan-Sharpening Method Based on Adaptive Injection of À trous Wavelet Decomposition , 2016 .

[16]  Geoffrey J. Hay,et al.  Object-based change detection , 2012 .

[17]  Kumaraswamy Nanthakumar,et al.  Feature-based MRI data fusion for cardiac arrhythmia studies , 2016, Comput. Biol. Medicine.

[18]  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.

[19]  Vince D. Calhoun,et al.  Multimodal Data Fusion Using Source Separation: Application to Medical Imaging , 2015, Proceedings of the IEEE.

[20]  R. J. Bhiwani,et al.  Image Fusion in Remote Sensing Applications: A Review , 2015 .

[21]  Miloud Chikr El-Mezouar.  Fusion d'images en télédétection satellitaire , 2012 .

[22]  Kenneth Grogan,et al.  A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring , 2016, Remote. Sens..

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

[24]  Gabriel Cristóbal,et al.  Texture classification using discrete Tchebichef moments. , 2013, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  Luciano Alparone,et al.  Advantages of Laplacian pyramids over ''à trous'' wavelet transforms for pansharpening of multispectral images , 2012, Remote Sensing.

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

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

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

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