Document Type: Original Research Paper


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



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.


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.