Smart pansharpening approach using kernel-based image filtering

Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previ-ous research in terms of subjective and objective assessments on remote sensing data.

[1]  Chen Chen,et al.  Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion , 2020, Inf. Fusion.

[2]  Guoping Qiu,et al.  Side window guided filtering , 2019, Signal Process..

[3]  Junjun Jiang,et al.  FusionGAN: A generative adversarial network for infrared and visible image fusion , 2019, Inf. Fusion.

[4]  Guoping Qiu,et al.  Side Window Filtering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Xiaohua Qiu,et al.  Guided filter-based multi-focus image fusion through focus region detection , 2019, Signal Process. Image Commun..

[6]  Ahed Abugabah,et al.  Intelligent Information Systems and Image Processing: A Novel Pan-Sharpening Technique Based on Multiscale Decomposition , 2018, ICVIP.

[7]  Arun Kumar Sangaiah,et al.  Pansharpening using a guided image filter based on dual-scale detail extraction , 2018 .

[8]  Sergio Escalera,et al.  Recurrent neural networks for remote sensing image classification , 2018, IET Comput. Vis..

[9]  G. Reddy Satellite Remote Sensing Sensors: Principles and Applications , 2018 .

[10]  Ahmad AL Smadi,et al.  Pansharpening via Deep Guided Filtering Network , 2018 .

[11]  Feiniu Yuan,et al.  Remote Sensing Image Fusion Based on Adaptive IHS and Multiscale Guided Filter , 2016, IEEE Access.

[12]  Shigang Yue,et al.  An effective pansharpening method based on guided filtering , 2016, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA).

[13]  Zhengguo Li,et al.  Gradient Domain Guided Image Filtering , 2015, IEEE Transactions on Image Processing.

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

[15]  Amlan Chakrabarti,et al.  A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images , 2015, ArXiv.

[16]  Shiqian Wu,et al.  Weighted Guided Image Filtering , 2016, IEEE Transactions on Image Processing.

[17]  A. Hegde,et al.  A Review of Quality Metrics for Fused Image , 2015 .

[18]  Jon Atli Benediktsson,et al.  Pansharpening With Matting Model , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Bo Li,et al.  High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Shutao Li,et al.  The multiscale directional bilateral filter and its application to multisensor image fusion , 2012, Inf. Fusion.

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

[23]  Gonzalo Seco-Granados,et al.  A Reduced Complexity Approach to IAA Beamforming for Efficient DOA Estimation of Coherent Sources , 2011, EURASIP J. Adv. Signal Process..

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

[25]  Zeev Farbman,et al.  Edge-preserving decompositions for multi-scale tone and detail manipulation , 2008, ACM Trans. Graph..

[26]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[27]  Jocelyn Chanussot,et al.  Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique , 2008, IEEE Geoscience and Remote Sensing Letters.

[28]  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.

[29]  Frédo Durand,et al.  A gentle introduction to bilateral filtering and its applications , 2007, SIGGRAPH Courses.

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

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

[32]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[33]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

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

[35]  Lucien Wald,et al.  Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions , 2002 .

[36]  Jong-Hyun Park,et al.  Image fusion using multiresolution analysis , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[37]  J. G. Liu,et al.  Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details , 2000 .

[38]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

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

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

[42]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  P. Chavez,et al.  Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis , 1989 .

[44]  Alan R. Gillespie,et al.  Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques , 1987 .

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

[46]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..