Document Type : Original Research Paper

Authors

Department of Geomatics, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

Abstract

Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis.
Methods: In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content.
Results: Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions.
Conclusion: it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.


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Copyrights
©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
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Keywords

Main Subjects

[1] G. Vivone, L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G.A. Licciardi, R. Restaino, L.Wald, “A Critical Comparison Among Pansharpening Algorithms,” IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2565-2586, 2015.

[2] S. Yang, M. Wang, L. Jiao, “Fusion of Multispectral and Panchromatic Images Based on Support Value Transform and Adaptive Principal Component Analysis” Information Fusion, 13(3): 177-184, 2012.

[3] M. Deshmukh, U. Behosale, “Image Fusion and Image Quality Assessment of Fused Images” International Journal of Image Processing (IJIP), 4(5): 484-508, 2010.

[4] O.A. Agudelo-Medina, H. Dario Benitez-Restrepo, G. Vivone, A. Bovik, “Perceptual Quality Assessment of Pan-Sharpened Images,” Remote Sens. 11(7): 1-19, 2019.

[5] A. Makarau, G. Palubinskas, P. Reinartz, “Analysis and selection of pan-sharpening assessment measures,” Journal of Applied Remote Sensing, 6 (1): 1-20, 2012.

[6] C. Pohl, J.L. Van Jenderen, “Review article Multisensor image fusion in remote sensing: concepts, methods and applications,” International Journal of Remote Sensing, 19(5): 823 -854, 2010.

[7] S. Aghapour Maleki, H. Ghassemian, “A critical review of quality assessment protocols in pan-sharpening,” in Proc. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W18, Joint Conferences of SMPR and GI Research: 13-18, 2019.

[8] G. Palubinskas, “Quality assessment of pan-sharpening methods,” presented at 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 2014.

[9] P. Mhangara, W. Mapurisa, N. Mudau, “Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery,” Applied Sciences, 10(5): 1-13, 2020.

[10]  A.G. Sulaiman, W.H. Elashmawi, G.S. El-Tawel, “A Robust Pan-Sharpening Scheme for Improving Resolution of Satellite Images in the Domain of the Nonsubsampled Shearlet Transform,” Sensing and Imaging, 21(3), 2019.

[11] S.M.A. Wady,Y. Bentoutou, A. Bengermikh, A. Bounoua, N. Taleb, “A new HIS and wavelet based pansharpening algorithm for high
spatial resolution satellite imagery,” Advances in space research, 66(7): 1507-1521, 2020
.

[12] Y. Choi, E. Sharifahmadian, Sh. Latifi, “Quality Assessment of Image Fusion Methods in Transform Domain,” International Journal on Information Theory (IJIT), 3(1): 7-18, 2014.

[13] S. R. Dammavalam, S. Maddala, K. Prasad MHM, “Quality assessment of pixel-level image fusion using fuzzy logic,” International Journal on Soft Computing ( IJSC ), 3(1): 13-15, 2012.

[14] H.B. Mitchell, “Image fusion, theories, techniques and applications,” Springer-Verlag Berlin Heidelberg, Germany, 2010.

[15] T. Stathaki, “Image fusion, algorithms and applications,” Academic Press is an imprint of Elsevier, Britain, 2008.

[16] M.R. Metwalli, A.H. Nasr, O.S. Farag Allah, S. El-Rabaie, “Image Fusion Based on Principal Component Analysis and High-Pass Filter,” International Conference on Computer Engineering & Systems, Cairo: 63-70, 2009.

[17] V.P.S. Naidu, and J.R. Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis,” Defence Science Journal, Vol. 58 (3), pp. 338-352, 2008.

[18] T.M. Tu, S.C. Su, H.C. Shyu, P.S. Huang, “A New Look at IHS- Like Image Fusion Method,” Information Fusion, 2(2001): 177-186, 2001.

[19] C. Yang, Q. Zhan, H. Liu, R. Ma, “An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing,” Sensors, 18(11): 1-20, 2018.

[20] F. Samadzadegan, F. Tabib Mahmoudi, “Data Fusion in remote sensing concepts and techniques,” University of Tehran Press 3316, 2nd Edition, 2014.


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