Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

Authors

DOI:

https://doi.org/10.4108/eetsis.5496

Keywords:

Pan-sharpening, Multispectral images, Panchromatic image, Landsat-8, Sentinel-2, Remote Sensing, Image Quality Assesment Metrics

Abstract

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data.

OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images.

METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.

RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result.

CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

References

C. Pohl and J. L. Van Genderen, “Review article Multisensor image fusion in remote sensing: Concepts, methods and applications,” International Journal of Remote Sensing, vol. 19, no. 5. pp. 823–854, 1998. doi: 10.1080/014311698215748.

P. Jagalingam and A. V. Hegde, “A Review of Quality Metrics for Fused Image,” Aquat Procedia, vol. 4, pp. 133–142, 2015, doi: 10.1016/j.aqpro.2015.02.019.

J. and A. V. H. Pushparaj, “ Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery,” Arabian Journal of Geosciences, vol. 10, no. 5, pp. 1–17, 2017.

F. DadrasJavan and F. Samadzadegan, “An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery,” Advances in Space Research, vol. 54, no. 11, pp. 2286–2295, Dec. 2014, doi: 10.1016/J.ASR.2014.08.024.

F. Alidoost, M. A. Sharifi, and A. Stein, “Region- and pixel-based image fusion for disaggregation of actual evapotranspiration,” http://dx.doi.org/10.1080/19479832.2015.1055834, vol. 6, no. 3, pp. 216–231, Jul. 2015, doi: 10.1080/19479832.2015.1055834.

F. Dadrass Javan, F. Samadzadegan, S. Mehravar, A. Toosi, R. Khatami, and A. Stein, “A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 171, pp. 101–117, Jan. 2021, doi: 10.1016/J.ISPRSJPRS.2020.11.001.

H. Li, L. Jing, and Y. Tang, “Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion,” Sensors 2017, Vol. 17, Page 89, vol. 17, no. 1, p. 89, Jan. 2017, doi: 10.3390/S17010089.

M. Hasanlou and M. R. Saradjian, “Quality assessment of pan-sharpening methods in high-resolution satellite images using radiometric and geometric index,” Arabian Journal of Geosciences, vol. 9, no. 1, pp. 1–10, Jan. 2016, doi: 10.1007/S12517-015-2015-0/FIGURES/8.

C. Pohl and J. L. (John L. ) Van Genderen, Remote sensing image fusion : a practical guide.

M. Belgiu and A. Stein, “Spatiotemporal image fusion in remote sensing,” Remote Sensing, vol. 11, no. 7. MDPI AG, 2019. doi: 10.3390/rs11070818.

V. Pandit, V. R. Pandit, and R. J. Bhiwani, “Study of Remote Sensing Image Fusion View project Design and Development of Embedded Systems View project Image Fusion in Remote Sensing Applications: A Review,” Int J Comput Appl, vol. 120, no. 10, pp. 975–8887, 2015, doi: 10.5120/21263-3846.

G. Kaplan, “Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery,” Environmental Sciences Proceedings 2021, Vol. 3, Page 64, vol. 3, no. 1, p. 64, Nov. 2020, doi: 10.3390/IECF2020-07888.

G. Kaur, K. S. Saini, D. Singh, and M. Kaur, “A Comprehensive Study on Computational Pansharpening Techniques for Remote Sensing Images,” Archives of Computational Methods in Engineering, vol. 28, no. 7, pp. 4961–4978, Dec. 2021, doi: 10.1007/S11831-021-09565-Y/TABLES/3.

H. Ghassemian, “A review of remote sensing image fusion methods,” Information Fusion, vol. 32. Elsevier B.V., pp. 75–89, Nov. 01, 2016. doi: 10.1016/j.inffus.2016.03.003.

Yuhendra, I. Alimuddin, J. T. S. Sumantyo, and H. Kuze, “Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data,” International Journal of Applied Earth Observation and Geoinformation, vol. 18, no. 1, pp. 165–175, Aug. 2012, doi: 10.1016/J.JAG.2012.01.013.

J. Duran, A. Buades, B. Coll, C. Sbert, and G. Blanchet, “A survey of pansharpening methods with a new band-decoupled variational model,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 125, pp. 78–105, Mar. 2017, doi: 10.1016/J.ISPRSJPRS.2016.12.013.

Q. Du, N. H. Younan, R. King, and V. P. Shah, “On the performance evaluation of pan-sharpening techniques,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 518–522, Oct. 2007, doi: 10.1109/LGRS.2007.896328.

G. Pinheiro and S. Minz, “Image Quality Evaluation of Various Pan-Sharpening Techniques Using Landsat-8 Imagery,” pp. 391–403, 2023, doi: 10.1007/978-981-99-1620-7_31.

G. Pinheiro and S. Minz, “Image Quality Assessment Of Spatiotemporal Image Fusion: A Case Study Approach Using Landsat-8 And Sentinel-2,” in 43rd Asian Conference on Remote Sensing 2022 (ACRS2022), Mongolia: Asian Association on Remote Sensing (AARS), 2022. Accessed: Mar. 16, 2023. [Online].

I. H. Rather and S. Kumar, “Generative adversarial network based synthetic data training model for lightweight convolutional neural networks,” Multimed Tools Appl, pp. 1–23, May 2023, doi: 10.1007/S11042-023-15747-6/TABLES/3.

N. H. Kaplan and I. Erer, “Aǧirliklandirilmiş Dalgacik Dönüşümü ile Pankeskinleştirme,” 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, pp. 781–784, Jun. 2016, doi: 10.1109/SIU.2016.7495856.

H. R. Shahdoosti and N. Javaheri, “Pansharpening of Clustered MS and Pan Images Considering Mixed Pixels,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 6, pp. 826–830, Jun. 2017, doi: 10.1109/LGRS.2017.2682122.

S. D. Jawak and A. J. Luis, “A Comprehensive Evaluation of PAN-Sharpening Algorithms Coupled with Resampling Methods for Image Synthesis of Very High Resolution Remotely Sensed Satellite Data,” Advances in Remote Sensing, vol. 2013, no. 04, pp. 332–344, Dec. 2013, doi: 10.4236/ARS.2013.24036.

Q. Xu, Y. Zhang, and B. Li, “Recent advances in pansharpening and key problems in applications,” http://dx.doi.org/10.1080/19479832.2014.889227, vol. 5, no. 3, pp. 175–195, 2014, doi: 10.1080/19479832.2014.889227.

A. Raj and S. Minz, “A Scalable Unsupervised Classification Method Using Rough Set for Remote Sensing Imagery,” International Journal of Software Science and Computational Intelligence, vol. 13, no. 2, pp. 65–88, Apr. 2021, doi: 10.4018/IJSSCI.2021040104:

G. Kaplan and U. Avdan, “Sentinel-2 Pan Sharpening—Comparative Analysis,” Proceedings 2018, Vol. 2, Page 345, vol. 2, no. 7, p. 345, Mar. 2018, doi: 10.3390/ECRS-2-05158.

Q. Wang, W. Shi, Z. Li, and P. M. Atkinson, “Fusion of Sentinel-2 images,” Remote Sens Environ, vol. 187, pp. 241–252, Dec. 2016, doi: 10.1016/J.RSE.2016.10.030.

M. Selva, B. Aiazzi, F. Butera, L. Chiarantini, and S. Baronti, “Hyper-sharpening: A first approach on SIM-GA data,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 8, no. 6, pp. 3008–3024, Jun. 2015, doi: 10.1109/JSTARS.2015.2440092.

M. Gašparović and T. Jogun, “The effect of fusing Sentinel-2 bands on land-cover classification,” https://doi.org/10.1080/01431161.2017.1392640, vol. 39, no. 3, pp. 822–841, Feb. 2017, doi: 10.1080/01431161.2017.1392640.

N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sens Environ, vol. 202, pp. 18–27, Dec. 2017, doi: 10.1016/J.RSE.2017.06.031.

L. Kumar and O. Mutanga, “Google Earth Engine Applications Since Inception: Usage, Trends, and Potential,” Remote Sensing 2018, Vol. 10, Page 1509, vol. 10, no. 10, p. 1509, Sep. 2018, doi: 10.3390/RS10101509.

H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco, “Google Earth Engine for geo-big data applications: A meta-analysis and systematic review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 152–170, Jun. 2020, doi: 10.1016/J.ISPRSJPRS.2020.04.001.

G. Pinheiro, A. Raj, · Sonajharia Minz, T. Choudhury, and J.-S. Um, “Inundation extend mapping for multi-temporal SAR using automatic thresholding and change detection: a case study on Kosi river of India,” Spatial Information Research, vol. 1, p. 3, doi: 10.1007/s41324-023-00555-9.

A. Capolupo and E. Tarantino, “Landsat 9 Satellite Images Potentiality in Extracting Land Cover Classes in GEE Environment Using an Index-Based Approach: The Case Study of Savona City,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14107 LNCS, pp. 251–265, 2023, doi: 10.1007/978-3-031-37114-1_17/COVER.

L. Lin et al., “Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine,” Applied Sciences 2020, Vol. 10, Page 7336, vol. 10, no. 20, p. 7336, Oct. 2020, doi: 10.3390/APP10207336.

L. Korhonen, Hadi, P. Packalen, and M. Rautiainen, “Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index,” Remote Sens Environ, vol. 195, pp. 259–274, Jun. 2017, doi: 10.1016/J.RSE.2017.03.021.

U. G. , P. S. P. and D. W. Holcomb. Gangkofner, “Optimizing the high-pass filter addition technique for image fusion,” Photogrammetric Engineering & Remote Sensing , vol. 73, no. 9, pp. 1107–1118, 2007.

G. Sarp, “Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul,” http://dx.doi.org/10.5721/EuJRS20144702, vol. 47, no. 1, pp. 19–28, Feb. 2017, doi: 10.5721/EUJRS20144702.

J. G. Liu, “Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details,” http://dx.doi.org/10.1080/014311600750037499, vol. 21, no. 18, pp. 3461–3472, 2010, doi: 10.1080/014311600750037499.

L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, and M. Selva, “Multispectral and panchromatic data fusion assessment without reference,” Photogramm Eng Remote Sensing, vol. 74, no. 2, pp. 193–200, 2008, doi: 10.14358/PERS.74.2.193.

S. Panchal and R. Thakker, “Signal & Image Processing,” An International Journal (SIPIJ), vol. 6, no. 5, 2015, doi: 10.5121/sipij.2015.6503.

Downloads

Published

21-03-2024

How to Cite

1.
Pinheiro G, Rather IH, Raj A, Minz S, Kumar S. Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8 . EAI Endorsed Scal Inf Syst [Internet]. 2024 Mar. 21 [cited 2024 Apr. 18];. Available from: https://publications.eai.eu/index.php/sis/article/view/5496

Issue

Section

Research articles