A method of panchromatic image modification for satellite imagery data fusion

Remote Sensing

Volumn 9, Issue 6, 2017, Pages

  Grochala, Aleksandra ^a   Kedzierski, Michal ^a  

^a MILITARY UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Data fusion; Gram Schmidt; Intensity; Pansharpening; Principal component analysis; Spatial resolution; Spectral quality

Indexed keywords

DATA FUSION; DATA INTEGRATION; IMAGE RESOLUTION; MEAN SQUARE ERROR; NUMERICAL METHODS; QUALITY CONTROL; SATELLITE IMAGERY; SATELLITES;

GRAM-SCHMIDT; INTENSITY; PAN-SHARPENING; SPATIAL RESOLUTION; SPECTRAL QUALITY;

PRINCIPAL COMPONENT ANALYSIS;

EID: 85021049769     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9060639     Document Type: Article

References (44)

1. Mikrut, S. The Influence of JPEG Compression on the Automatic Extraction of Cropland Boundaries with Subpixel Accuracy Using Multispectral Images. In Geodesy and Environmental Engineering Commission; Polish Academy of Science-Cracow Branch: Krakow, Poland, 2006; pp. 97-111
2. Aiazzi, B.; Baronti, S.; Selva, M. Improving component substitution pansharpening through multivariate regression of MS + Pan data. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3230-3239
3. Zhang, Y.; Hong, G. An IHS and wavelet integrated approach to improve pansharpening visual quality of natural colour IKONOS and QuickBird images. Inf. Fusion 2005, 6, 225-234
4. Chavez, P.S.; Sides, S.C.; Anderson, A.J. Comparisation of Three Different Methods to Merge Multiresolution and Multispectral Data: Landsat TM and SPOT Panchromatic. Photogramm. Eng. Remote Sens. 1991, 57, 265-303
5. Li, X.; Li, L.; He, M. A Novel Pansharpening Algorithm for WorldView-2 Satellite Images. Available online: http://www.ipcsit.com/vol31/004-ICIII2012-C0010.pdf (accessed on 20 June 2017)
6. Bobkowska, K.; Przyborski, M.; Szulwic, J. A Method of Selecting Light Sources from Night Satellite Scenes. In Proceedings of the SGEM 2015 GeoConference Ecology and Environmental Protection, Albena, Bulgaria, 18-24 June 2015; pp. 1314-2704
7. Kedzierski, M.;Wilinska, M.;Wierzbicki, D.; Fryskowska, A.; Delis, P. Image Data Fusion for Flood Plain Mapping. In Proceedings of the 9th International Conference on Environmental Engineering, Vilnius, Lithuania, 22-23 May 2014
8. Jenerowicz, A.; Woroszkiewicz, M. The Pan-Sharpening of Satellite and UAV Imagery for Agricultural Applications. In SPIE Remote Sensing; International Society for Optics and Photonics: Bellingham, WA, USA, 2016
9. Fonseca, L.; Namikawa, L.; Castejon, E.; Carvalho, L.; Pinho, C.; Pagamisse, A. Image Fusion for Remote Sensing Applications. In Image Fusion and Its Applications; Zheng, Y., Ed.; InTech: Rijeka, Croatia, 2011
10. Shi, W.; Zhu, C.; Tian, Y.; Nichol, J. Wavelet-based image fusion and quality assessment. Int. J. Appl. Earth Obs. Geoinf. 2005, 6, 241-251
11. Zhang, H.K.; Huang, B. A new look at image fusion methods from a Bayesian perspective. Remote Sens. 2015, 7, 6828-6861
12. Helmy, A.K.; El-Tawel, G.S. An integrated scheme to improve pan-sharpening visual quality of satellite images. Egypt. Inf. J. 2015, 16, 121-131
13. Jelének, J.; Kopačková, V.; Koucká, L.; Mišurec, J. Testing a modified PCA-based sharpening approach for image fusion. Remote Sens. 2016, 8
14. Fryskowska, A.; Wojtkowska, M.; Delis, P.; Grochala, A. Some Aspects of Satellite Imagery Integration from EROS B and LANDSAT 8. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 12-19 July 2016; pp. 647-652
15. Borkowski, A.; Glowienka, E.; Hejmanowska, B.; Kwiatkowska-Malina, J.; Kwolek, M.; Michalowska, K.; Mikrut, S.; Pekala, A.; Pirowski, T.; Zabrzeska-Gasiorek, B. GIS and Remote Sensing in Environmental Monitoring; Glowienka, E., Ed.; Rzeszow School of Engineering and Economics, Neiko Print & Publishing: Tarnobrzeg, Poland, 2015; pp. 7-48
16. Israa, A.; Javier, M. Multispectral Image Pansharpening based on the Contourlet Transform. In Information Optics and Photonics; Springer: New York, NY, USA, 2010; pp. 247-261
17. Pohl, C. Tools and Methods Used in Data Fusion. In Future Trends in Remote Sensing; Springer: Rotterdam, The Netherlands, 1998; Volume 32, pp. 391-399
18. Pohl, C. Tools and Methods for Fusion of Images of different Spatial Resolution. In Proceedings of the International Archives of Photogrammetry and Remote Sensing, Valladolid, Spain, 3-4 June 1999
19. Liu, J.G.; Mason, P.J. Essential Image Processing and GIS for Remote Sensing; Willey-Blackwell: London, UK, 2009; pp. 57-85
20. Jolliffe, I. Principal Component Analysis; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2002
21. Kazimierski, W.; Stateczny, A. Fusion of Data from AIS and Tracking Radar for the Needs of ECDIS. In Proceedings of the 2013 Signal Processing Symposium (SPS), Piscataway, NJ, USA, 5-7 June 2013; pp. 1-6
22. Xie, B.; Zhang, H.K.; Huang, B. Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods. Remote Sens. 2017, 9
23. Du, Q.; Younan, N.H.; King, R.; Shah, V.P. On the performance evaluation of pan-sharpening techniques. IEEE Geosci. Remote Sens. Lett. 2007, 4, 518-522
24. Stathaki, T. Image Fusion: Algorithms and Applications; Elsevier: Amsterdam, The Netherlands, 2011; pp. 36-38
25. Pirowski, T. Rank of fusion methods of remotely sensed images of various resolution-Formal assessment of merging Landsat TM and IRS-PAN data. Arch. Photogramm. Remote Sens., 2009, 20, 343-358
26. Maurer, T. How to pan-sharpen images using the Gram-Schmidt pan-sharpen method-a recipe. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hannover, Germany, 21-24 May 2013
27. You, X.; Du, L.; Cheung, Y.M.; Chen, Q. A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks. IEEE Trans. Image Process. 2010, 19, 3271-3284
28. Dong, L.; Yang, Q.; Wu, H.; Xiao, H.; Xu, M. High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform. Neurocomputing 2015, 159, 268-274
29. Li, S.; Yang, B. Hybrid multiresolution method for multisensor multimodal image fusion. IEEE Sens. J. 2010, 10, 1519-1526
30. Helmy, A.K.; Nasr, A.H.; El-Taweel, G.S. Assessment and Evaluation of Different Data Fusion Techniques. Int. J. Comput. 2010, 4, 107-115
31. Wald, L.; Ranchin, T.; Mangolini, M. Fusion of Satellite Images of Different Spatial Resolutions: Assessing in quality of resulting images. Photogramm. Eng. Remote Sens. 1997, 63, 691-699
32. Wald, L. Quality of High Resolution Synthesised Images: Is There a Simple Criterion? Available online: https://hal.archives-ouvertes.fr/hal-00395027/document (accessed on 20 June 2017)
33. Ranchin, T.; Wald, L. Fusion of High Spatial and Spectral Resolution Images: The ARSIS Concept and its Implementation. Photogramm. Eng. Remote Sens. 2000, 66, 49-61
34. Pirowski, T. The integration of remote sensing data acquired with various sensors-A proposal of merged image assessment. Geoinform. Pol. 2006, 8, 59-75
35. Hnatushenko, V.V.; Vasyliev, V.V. Remote Sensing Image Fusion Using ICA and Optimized Wavelet Transform. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 12-19 July 2016; pp. 653-659
36. Yakhdani, M.F.; Azizi, A. Quality Assessment of Image Fusion Techniques for Multisensor High Resolution Satellite Images-Case Study: IRS-P5 and IRS-P6 Satellite Images. In ISPRS TC VII Symposium-100 Years ISPRS;Wagner, W., Székely, B., Eds.; IAPRS: Vienna, Austria, 2010; Volume 37, pp. 204-209
37. Han, S.S.; Li, H.T.; Gu, H.Y. The Study on Image Fusion for High Spatial Resolution Remote Sensing Images. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, China, 3-11 July 2008; pp. 1159-1163
38. Wang, Z.; Bovik, A.C.; Lu, L. Why is image quality assessment so difficult? In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Orlando, FL, USA, 13-17 May 2002
39. Osińska-Skotak, K. Assessment of different image fusion methods on example WorldView-2 images. Arch. Photogramm. Remote Sens. 2012, 24, 231-244
40. NASA. Available online: http://landsat.gsfc.nasa.gov (accessed on 10 January 2017)
41. Lillo-Saavedra, M.; Gonzalo, L. Spectral or spatial quality for fused satellite imagery? A trade-off solution using the wavelet à trous algorithm. Int. J. Remote Sens. 2006, 27, 1453-1464
42. Tu, T.M.; Lee, Y.C.; Chang, C.P.; Huang, P.S. Adjustable intensity-hue-saturation and Brovey transform fusion technique for IKONOS/QuickBird imagery. Opt. Eng. 2005, 44, 116201
43. Yang, J.; Zhang, J.; Huang, G. A parallel computing paradigm for pan-sharpening algorithms of remotely sensed images on a multi-core computer. Remote Sens. 2014, 6, 6039-6063
44. Alparone, L.; Baronti, S.; Garzelli, A.; Nencini, F. A global quality measurement of pan-sharpened multispectral imagery. IEEE Geosci. Remote Sens. Lett. 2004, 1, 313-317