Fusion of Remotely Sensed Images using Wavelet Transforms and Decorrelated Multispectral Channels
DOI:
https://doi.org/10.18372/1990-5548.75.17558Keywords:
remote sensing, multispectral image, panchromatic image, fusion image, wavelet-decomposition, wavelet-synthesis, decorrelation, signal entropyAbstract
The article is devoted to the development of mathematical models based on combining images obtained by remote sensing means with different spatial and radiometric resolutions. An analysis of modern means of remote sensing, which form images that are fixed under the same positional conditions of projection, in different spectral ranges of radiation, was carried out. Images formed in a wide spectral range and have a higher linear resolution than images formed in narrower ranges, but the latter contain spectral information. An applied model of combining images captured in different spectral ranges using the pyramidal wavelet transform has been developed. The optimal model of decorrelation of spectral channels of multispectral images based on signal entropy was determined.
References
R. Mahler, “Optimal/robust distributed data fusion: a unified approach,” Proceedings of SPIE, vol. 4052: Signal Processing, Sensor Fusion, and Target Recognition, no. 4, 2000, pp. 128–138. https://doi.org/10.1117/12.395064
V. K. Shettigara, “A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set,” Photogrammetry engineering and remote sensing. vol. 58, no. 5, 1992, pp. 561–567.
C. Pohl and J. L. Van Genderen, “Multisensor image fusion in remote sensing: concepts, methods and applications,” International journal of remote sensing. vol. 19, no. 5, 1998, pp. 823–854. https://doi.org/10.1080/014311698215748
K. Vani, S. Shanmugavel, M. Marruthachalam, and Vani K. “Fusion of IRS-LISS and pan images using different resolution ratios,” Proceedings of the 22nd Asian Conf. on Remote Sensing, Singapore, vol. 1, 2001, pp. 146–151.
J. S. Lee and K. Hoppel, “Principal components transformation of multifrequency polarunetric SAR imagery,” IEEE Trans. Geosci. Remote Sensing, no. 30, 1992, pp. 686–696. https://doi.org/10.1109/36.158862
W. Pratt, Digital image processing, 3 ed., Wiley, 2001, 725 p. https://doi.org/10.1002/0471221325
A. H. Pellemans, R. W. Jordans, R. Allewijn, “Merging multispectral and panchromatic SPOT images with respect to the radiometric properties of the sensor,” Photogrammetric engineering and remote sensing, vol. 59, no. 1, pp. 81–87, 1993.
M. Chiarella, D. A. Fay, R. T. Ivey, N. A. Bomberger, and A. M. Waxman, “Multi-sensor image fusion, mining, & reasoning: Rule sets for higher-level AFE in a COTS environment,” Proceedings of the 7th International Conference on Information Fusion, Sweden, Stockholm, 2004, pp. 983–990.
C.-M. Chen, G. F. Hepner, R. R. Forster, “Fusion of hyperspectral and radar data using the IHS transformation to enhance urban surface features,” ISPRS J. Photogramm. Remote Sensing, vol. 58, no. 1–2, pp. 19–30, 2003. https://doi.org/10.1016/S0924-2716(03)00014-5
P. J. Burt and E. H. Adelson, “The laplacian pyramid as a compact image code,” IEEE Trans. on Comm., vol. 31, no. 4, 1983, pp. 532–540. https://doi.org/10.1109/TCOM.1983.1095851
K. Charles Chui, An Introduction to Wavelets, Academic Press, San Diego, 1992.
Daubechies, Ten Lectures on Wavelets, SIAM, vo1. 61, Philadelphia, 1992. https://doi.org/10.1137/1.9781611970104
G. Strang and T. Nguyen, Wavelets and filter banks, Wellesly-Cambridge press, 1997, 520 p.
S. Mallat, A wavelet tour of signal processing, Academic Press., 1999, 851 p. https://doi.org/10.1016/B978-012466606-1/50008-8
G. Strang and T. Nguyen, Wavelets and filter banks, Wellesly-Cambridge press, 1997, 520 p.
B. Girod, F. Hartung, and U. Horn, Subband image coding. In Subband and wavelet transforms: design and applications, Boston, MA: Kluwer Academic Publishers, 1995, 472 p. https://doi.org/10.1007/978-1-4613-0483-8_7
B. Aiazzi, L. Alparone, F. Argenti, and S. Baronti, “Wavelet and pyramid techniques for multisensor data fusion: a performance comparison varying with scale ratios,” Proceedings of SPIE, vol. 3871: Image and Signal Processing for Remote Sensing, pp. 251–262, 1999. https://doi.org/10.1117/12.373263
P. Hill, N. Canagarajah, and D. Bull, Image fusion using complex wavelets, http://www.bmva.ac.uk/bmvc/2002/papers/88/full_88.pdf. https://doi.org/10.5244/C.16.47
G. Hong and Y. Zhang, “Effects of different types of wavelets on image fusion,” Proceedings of XXth ISPRS Congress, Istanbul, Turkey, 2004, pp. 915–920.
T. Z. Wei, W. J. Guo, and H. S. Li, “The wavelet transform application for image fusion,” Proceedings of SPIE, vol. 4058, no. 4, 2000, pp. 462–469.
A. Garzelli, “Wavelet-based fusion of optical and SAR image data over urban area,” Proceedings of ISPRS Commission III, Symposium: Photogrammetric Computer Vision, Graz, Austria, 2002, pp. B-59.
M. Gonzalez de Audicana and A. Seco, “Fusion of multispectral and panchromatic images using wavelet transform. Evaluation of crop classification accuracy,” Proceedings of 22nd EARSeL Annual Symposium: Geoinformation for European-wide integration, Prague, Czech Republic, 2002, pp. 265–272.
J. Nunez, X. Otazu, O. Fors, F. Prades, V. Pala, R. Arbiol, “Multiresolution-based image fusion with additive wavelet decomposition,” IEEE Transactions on geoscience and remote sensing, vol. 37, no. 3, 1999, pp. 1204–1211. https://doi.org/10.1109/36.763274
R. L. King and J. Wang, “A wavelet based algorithm for PAN sharpening Landsat 7 imagery, IEEE International, vol. 2, 2001, pp. 849–851.
C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 623–656, July-Oct. 1948. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
A. Ja. Hinchin, Matematicheskie osnovanija teorii informacii, Fizmatgiz, 1954, 560 p. [in Russian]
V. M. Korchynskyi and O. M. Hordiienko, “Pidvyshchennia informatyvnosti proektsiinykh rastrovykh zobrazhen,” Prykladna heometriia ta inzhenerna hrafika. Pratsi Tavriiskoi derzhavnoi ahrotekhnichnoi akademii, vol. 4, no. 25, Melitopol: TDATA, 2004, pp. 33–37 [in Ukrainian].
O. M. Hordiienko, “Vplyv parametriv funktsionalnykh veivlet-bazysiv na pidvyshchennia informatyvnosti proektsiinykh rastrovykh zobrazhen,” Heometrychne ta kompiuterne modeliuvannia. Kharkivskyi derzhavnyi universytet kharchuvannia ta torhivli, Kharkiv, vol. 8, 2004, pp. 96–100. [in Ukrainian]
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