Pansharpening through orthogonal projection of data

Abstract

With the increase in the amount of satellite data particularly in the form of satellite images, the need to fuse heterogeneous imagery has become an important research area. Pansharpening is an image fusion method that involves fusing a high spatial resolution panchromatic imagery and a high spectral resolution multispectral imagery to obtain an image that possesses spatial and spectral data both in high resolution. In this paper, a pansharpening method based on a classical information-theoretic result of orthogonal projection between two sets of correlated data is proposed. The originality of the study lies in the application of the information-theoretic approach to pansharpening which has not been reported to date. The proposed method which is illustrated using IKONOS data is also compared favorably with existing pansharpening methods such as IHS, Brovey, PCA, SFIM, HPF, and Multi methods using standard evaluation criteria, such as Chi-square test (X2), R2 test, RMSE, SNR, spectral discrepancy (SD) and ERGAS.

Keywords

• Data fusion
• IKONOS
• Image fusion
• Information correlation
• Pansharpening

References

1. [1] Aanaes H, Sveinsson JR, Nielsen AA, Bovith T, Benediktsson JA. Model-Based Satellite Image Fusion. IEEE Trans Geosci Remote Sens. 2008; 46(5): 1336–46.
2. [2] Hong G, Zhang Y, Mercer B. A Wavelet and IHS Integration Method to Fuse High-Resolution SAR with Moderate Resolution Multispectral Images. Photogramm Eng Remote Sens. 2009; 75(10): 1213–23.
3. [3] Helmy AK, El-tawel GS. An integrated scheme to improve pan-sharpening visual quality of satellite images. Egypt Informatics J. 2015; 121–31.
4. [4] Ourabia S, Boumediene TH, Smara Y. A new Pansharpening Approach Based on NonSubsampled Contourlet Transform Using Enhanced PCA Applied to SPOT and ALSAT-2A Satellite. J Indian Soc Remote Sens. 2016; 44(February) :665–674.
5. [5] Devi MB, Devanathan R. Pansharpening using data-driven model based on linear regression. 2018 IEEE Int Conf Electron Comput Commun Technol CONECCT 2018. 2018; 1–5.
6. [6] Bidyarani Devi M, Devanathan R. Pansharpening using data-centric optimization approach. Int J Remote Sens. 2019; 40(20): 7784–804. DOI: https://doi.org/10.1080/01431161.2019.1602794
7. [7] Pálsson F, Sveinsson JR, Member S, Benediktsson JA. Classification of Pansharpened Urban Satellite Images. IEEE J Sel Top Appl Earth Obs Remote Sens. 2012;5(1): 281–97.
8. [8] Garzelli A. A review of image fusion algorithms based on the super-resolution paradigm. Remote Sens. 2016;8(10):1.

9. [9] Addesso P, Vivone G, Restaino R, Chanussot J. A Data-Driven Model-Based Regression Applied to Panchromatic Sharpening. IEEE Trans Image Process. 2020;29:7779–94.
10. [10] Devi MB. Pansharpening With Panchromatic And Multispectral Remote Sensing Data. Hindustan Institute Of Technology And Science; 2020.
11. [11] Masi G, Cozzolino D, Verdoliva L, Scarpa G. Pansharpening by convolutional neural networks. Remote Sens. 2016; 8(7).
12. [12] He L, Rao Y, Li J, Chanussot J, Plaza A, Zhu J. Pansharpening via Detail Injection Based Convolutional Neural Networks. IEEE J Sel Top Appl Earth Obs Remote Sens. 2019;
13. [13] Xie J, He L. Two-Stage Fusion based CNN for Hyperspectral Pansharpening. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 2022. p. 1091–4.
14. [14] Li J, Sun W, Jiang M, Yuan Q. Self-Supervised Pansharpening Based on a Cycle-Consistent Generative Adversarial Network. IEEE Geosci Remote Sens Lett. 2022;19:1–5.
15. [15] Li X, Li Y, Shi G, Zhang L, Li W, Lei D. Pansharpening Method Based on Deep Nonlocal Unfolding. IEEE Trans Geosci Remote Sens. 2023; 61: 1–11.
16. [16] Hassibi B, Sayed AH, Kailath T. Linear estimation in Krein spaces-Part I: Theory. IEEE Trans Automat Contr. 1996; 41(1): 18–33.
17. [17] Carper WJ, Lillesand TM, Kiefer RW. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm Eng Remote Sens. 1990; 56(4): 459–67.
18. [18] Tu TM, Huang PS, Hung CL, Chang CP. A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geosci Remote Sens Lett. 2004; 1(4): 309–12.
19. [19] Gillespie AR, Kahle AB, Walker RE. Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sens Environ [Internet]. 1987, 22(3): 343–65.
20. [20] Pat S. Chavez, Jr. SCS and JA. Comparison of Three Different Methods to Merge Multiresolution and Multispectral Data: LANDSAT TM and SPOT Panchromatic: 1991; 57(3): 295–303.
21. [21] Yuan D, Hong X, Yu S, Li L, Zhao Y. Analysis of four remote image fusion algorithms for landsat7 ETM+ PAN and multi-spectral imagery. Int J Online Eng. 2014; 10(3): 49–52.
22. [22] Chavaz P., Jr. and Bowell J.A. Comparison of the spectral information content of Landsat thematic mapper and SPOT for three different sites in the Phoenix, Arizona region. Photogramm Eng Remote Sensing. 1988; 54(12)
23. [23] Edwards K, Davls PA. The Use of Intensity-Hue-Saturation Transformation for Producing Color-Shaded Relief Images. Photogramm Eng Remote Sens. 1994; 60: 1369–74.
24. [24] Plackett RL. Karl Pearson and the Chi-Squared Test. Int Stat Rev / Rev Int Stat. 1983.
25. [25] Aleksandra Grochala and Michal K. Satellite Imagery Data Fusion. Remote Sens. 2017; (9): 11–3.
26. [26] Yuhendra, Alimuddin I, Sumantyo JTS, Kuze H. Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. Int J Appl Earth Obs Geoinf [Internet]. 2012 Aug;18: 165–75.
27. [27] Yakhdani MF, Azizi A. Quality assessment of image fusion techniques for multisensor high-resolution satellite images (CASE STUDY : IRS-P5 AND IRS-P6. 2010; 38: 204–9.
28. [28] Alparone L, Wald L, Chanussot J, Thomas C, Gamba P, Bruce LM. Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest. IEEE Trans Geosci Remote Sens. 2007; 45(10): 3012–21.
29. [29] Du Q, Younan NH, King R, Shah VP. On the performance evaluation of pan-sharpening techniques. IEEE Geosci Remote Sens Let. 2007; 4(4): 518–22.