Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques

In remote sensing, image fusion technique is a useful tool used to fuse high spatial resolution panchromatic images (PAN) with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion (F) while preserving the spectral information in the multispectral image (MS).There are many PAN sharpening techniques or Pixel-Based image fusion techniques that have been developed to try to enhance the spatial resolution and the spectral property preservation of the MS. This paper attempts to undertake the study of image fusion, by using two types of pixel –based image fusion techniques i.e. Arithmetic Combination and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques. The first type includes Brovey Transform (BT), Color Normalized Transformation (CN) and Multiplicative Method (MLT). The second type include High-Pass Filter Additive Method (HPFA), High – Frequency- Addition Method (HFA) High Frequency Modulation Method (HFM) and The Wavelet transformbased fusion method (WT). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), EntropyሺEn ), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively.

[1]  Tania Stathaki,et al.  Image Fusion: Algorithms and Applications , 2008 .

[2]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[3]  K. Revathy,et al.  A Comparative Analysis of Image Fusion Techniques for Remote Sensed Images , 2007, World Congress on Engineering.

[4]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[5]  Scott E. Umbaugh,et al.  Computer Vision and Image Processing: A Practical Approach Using CVIPTools , 1997 .

[6]  Lucien Wald,et al.  Definitions and terms of reference in data fusion , 1999 .

[7]  Dafang Zhuang,et al.  Advances in Multi-Sensor Data Fusion: Algorithms and Applications , 2009, Sensors.

[8]  J. Vrabel,et al.  Multispectral Imagery Advanced Band Sharpening Study , 2000 .

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

[10]  J. Hill,et al.  A LOCAL CORRELATION APPROACH FOR THE FUSION OF REMO TE SENSING DATA WITH DIFFERENT SPATIAL RESOLUTIONS IN FORESTRY APPLICATIONS , 1999 .

[11]  Jun Li.  SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2010 .

[12]  Bruno Aiazzi,et al.  2 – Image fusion through multiresolution oversampled decompositions , 2008 .

[13]  Qian Yin,et al.  Mallat Fusion for Multi-Source Remote Sensing Classification , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[14]  Jun Li.  SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2000 .

[15]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[16]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[17]  Thierry Ranchin,et al.  On the assessment of merging processes for the improvement of the spatial resolution of multispectral SPOT XS images , 1996 .

[18]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[19]  I. Parcharidis,et al.  Landsat TM and ERS data fusion: a statistical approach evaluation for four different methods , 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).

[20]  Thierry Ranchin,et al.  The ARSIS method: a general solution for improving spatial resolution of images by the means of sensor fusion , 1996 .

[21]  Jianya Gong,et al.  Multivariate statistical analysis of measures for assessing the quality of image fusion , 2010 .

[22]  Hongde Liu,et al.  Evaluation of various image fusion techniques and imaging scales for forest features interpretation , 2001 .

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

[24]  M. Malik,et al.  Wavelet Based Exposure Fusion , 2008 .

[25]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[26]  Jyh-Horng Jeng,et al.  Region-Based Image Fusion with Artificial Neural Network , 2009 .

[27]  Krištof Oštir,et al.  High-resolution image fusion : Methods to preserve spectral and spatial resolution , 2006 .

[28]  Yaonan Wang,et al.  Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images , 2002, Inf. Fusion.

[29]  Roger L. King,et al.  A wavelet based algorithm for pan sharpening Landsat 7 imagery , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[30]  Y. Kiran Kumar,et al.  COMPARISON OF FUSION TECHNIQUES APPLIED TO PRECLINICAL IMAGES: FAST DISCRETE CURVELET TRANSFORM USING WRAPPING TECHNIQUE & WAVELET TRANSFORM , 2009 .

[31]  Nicolas H. Younan,et al.  Quality analysis of pansharpened images , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[32]  C. Pohl.  TOOLS AND METHODS FOR FUSION OF IMAGES OF DIFFERENT SPATIAL RESOLUTION , 1999 .

[33]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[34]  Luciano Alparone,et al.  Landsat ETM+ and SAR image fusion based on generalized intensity Modulation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Duane B. Carter.  Analysis of Multiresolution Data fusion Techniques , 1998 .

[36]  Jim. Vrabel,et al.  Multispectral imagery band sharpening study , 1996 .

[37]  李幼升,et al.  Ph , 1989 .

[38]  D. Amarsaikhan,et al.  Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification , 2010 .

[39]  S. Sangwine,et al.  The Colour Image Processing Handbook , 1998, Springer US.

[40]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[41]  L. Alparone,et al.  GENERALISED LAPLACIAN PYRAMID-BASED FUSION OF MS + P IMAGE DATA WITH SPECTRAL DISTORTION MINIMISATION , 2002 .

[42]  Roger L. King,et al.  Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[43]  M. Seetha,et al.  PERFORMANCE ANALYSIS OF IMAGE FUSION TECHNIQUES BY IMAGE SEGMENTATION , 2007 .

[44]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[45]  F. Nencini,et al.  Fusion of Panchromatic and Multispectral Images by Genetic Algorithms , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[46]  Jixian Zhang.  Multi-source remote sensing data fusion: status and trends , 2010 .

[47]  D. Holcomb,et al.  Optimizing the High-Pass Filter Addition Technique for Image Fusion , 2007 .

[48]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[49]  Manfred Ehlers,et al.  Multi-sensor image fusion for pansharpening in remote sensing , 2010 .

[50]  J. Hothmer.  Book reviewInternational archives of photogrammetry and remote sensing: ISPRS, Editor S. Murai: volume 27 part A, Tokyo-Japan-Japan 1989 , 1989 .

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

[52]  Wu Wenbo,et al.  STUDY OF REMOTE SENSING IMAGE FUSION AND ITS APPLICATION IN IMAGE CLASSIFICATION , 2008 .