AUTOMATED PANSHARPENING INFORMATION TECHNOLOGY OF SATELLITE IMAGES
DOI:
https://doi.org/10.15588/1607-3274-2021-2-13Keywords:
image fusion, satellite image, pansharpening, automating, WorldView, high resolution imaging, metanalysis.Abstract
Context. Nowadays, information technologies are widely used in digital image processing. The task of joint processing of satellite image obtained by different space systems that have different spatial differences is important. The already known pansharpening methods to improve the quality of the resulting image, there are new scientific problems associated with increasing the requirements for high-resolution image processing and the development of automated technology for processing the satellite data for further thematic analysis. Most spatial resolution techniques result in artifacts. Our work explores the major remote sensing data fusion techniques at pixel level and reviews the concept, principals, limitations and advantages for each technique with the program implementation of research.
Objective. The goal of the work is analyze the effectiveness of the traditional pan-sharpening methods like the Brovey, the wavelet-transform, the GIHS, the HCT and the combined pansharpening method for satellite images of high-spatial resolution.
Method. In this paper we propose the information technology for pansharpening high spatial resolution images with automation of choosing the best method based on the analysis of quantitative and qualitative evolutions. The method involves the scaling multispectral image to the size of the panchromatic image; using histogram equalization to adjust the primary images by aligning the integral areas of the sections with different brightness; conversion of primary images after the spectral correction on traditional pansharpening methods; analyze the effectiveness of the results obtained for conducts their detailed comparative visual and quantitative evaluation. The technology allows determining the best method of pansharpening by analyzing quantitative metrics: the NDVI index, the RMSE and the ERGAS. The NDVI index for the methods Brovey and HPF indicate color distortion in comparison with the reference data. This is due to the fact that the Brovey and HPF methods are based on the fusion of three channel images and do not include the information contained in the near infrared range. The RMSE and the ERGAS show the superiority of the combined HSVHCT-Wavelet method over the conventional and state-of-art image resolution enhancement techniques of high resolution satellite images. This is achieved, in particular, by preliminary processing of primary images, data processing localized spectral bases, optimized performance information, and the information contained in the infrared image.
Results. The software implementing proposed method is developed. The experiments to study the properties of the proposed algorithm are conducted. Experimental evaluation performed on eight-primary satellite images of high spatial resolution obtained WorldView-2 satellite. The experimental results show that a synthesized high spatial resolution image with high information content is achieved with the complex use of fusion methods, which makes it possible to increase the spatial resolution of the original multichannel image without color distortions.
Conclusions. The experiments confirmed the effectiveness of the proposed automated information technology for pansharpening high-resolution satellite images with the development of a graphical interface to obtain a new synthesized image. In addition, the proposed technology will effectively carry out further recognition and real-time monitoring infrastructure.
References
Kashtan V. Yu., Shedlovska Y. I., Hnatushenko V. V. Processing technology of multispectral remote sensing images [Electronic recourse], International Young Scientists Forum on Applied Physics 2017, October 16–20, Lviv, Ukraine: Proceedings. Lviv, 2017, pp. 355–358. DOI:10.1109/ysf.2017.8126647.
Zhang Y. Understanding image fusion, Photogrammetric engineering and remote sensing, 2004, Vol. 7, No. 6, pp. 657–661.
Palsson F., Sveinsson J. R., Ulfarsson M. O., Benediktsson J. A. MTF-based deblurring using a wiener filter for CS and MRA pansharpening methods, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, Vol. 9, No. 6, pp. 2255–2269.
Amro I., Mateos J., Vega M., Molina R., Katsaggelos A. K. A survey of classical methods and new trends in pansharpening of multispectral images, EURASIP Journal on Advances in Signal Processing, Springer Open Journal, 2011, No. 1, P. 79.
Kashtan V. Yu., Hnatushenko V. V. A Wavelet and HSV Pansharpening Technology of High Resolution Satellite Images, Intelligent Information Technologies & Systems of Information Security IntelITSIS 2020, Khmelnytskyi, Ukraine, June 10–12, 2020, pp. 67–76.
Hnatushenko V., Hnatushenko Vik., Kavats O., Shevchenko V. Pansharpening technology of high resolution multispectral and panchromatic satellite images, Scientific Bulletin of National Mining University, Issue 4, 2015, pp. 91–98.
Ghahremani M., Ghassemian H. Nonlinear IHS: A promising method for pan-sharpening, IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 11, 2016, pp. 1606–1610.
Ma J., Chen C., Li C., Huang J. Infrared and visible image fusion via gradient transfer and total variation minimization, Information Fusion, 2016, Vol. 31, pp. 100–109.
Rahmani S., Strait M., Merkurjev D., Moeller M., T. Wittman An adaptive IHS pansharpening method, IEEE Geosci. Remote Sens. Lett., 2010, Vol. 7, No. 4, pp. 746– 750.
Haitao Yin., Shutao Li Pansharpening with multiscale normalized nonlocal means filter: a two-step approach, IEEE Transactions on Geoscience and Remote Sensing, 2015, Vol. 53, No. 10, pp. 5734–5745.
Ghassemian H. A review of remote sensing image fusion methods, Information Fusion, 2016, 32, pp. 75–89. DOI:10.1016 /j.inffus.2016.03.003
Wang Z., Ziou D., Armenakis C., Li D., Li Q. A comparative analysis of image fusion methods, IEEE Transactions on Geoscience and Remote Sensing, 2005, Vol. 43, No. 6, pp. 1391–1402.
Li X., Xu F., Lyu X., Tong Y., Chen Z., Li S., Liu D. A Remote-Sensing Image Pan-Sharpening Method Based on Multi-Scale Channel Attention Residual Network, IEEE Access, 2020, No. 8, pp. 27163–27177. DOI:10.1109/access.2020.2971502.
Aishwarya N., Abirami S., Amutha R. Multifocus image fusion using Discrete Wavelet Transform and Sparse Representation, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai, 2016, pp. 2377–2382. DOI: 10.1109/WiSPNET.2016.7566567.
Meinel G., Neubert M. A comparison of segmentation programs for high resolution remote sensing data, International Archives of Photogrammetry and Remote Sensing, 2014, 35(Part B), pp. 1097–1105.
Hnatushenko V. V., Vasyliev V. V. Remote sensing image fusion using ICA and optimized wavelet transform, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI–B7, 2016, pp. 653–659, https://doi.org/10.5194/isprs-archivesXLI-B7-653-2016.
Hnatushenko V. V., Shevchenko V. Yu., Kavats O. O. Improvement the spatial resolution of multichannel aerospace high spatial resolution imageson the base of hyperspherical transform, Radio Electronics, Computer Science, Control, 2015, No. 1, pp. 73–78 DOI 10.15588/1607-3274-2015-110.
Tu T., Su S., Shyu H., Huang P. A new look at IHS-like image fusion methods, Information Fusion, 2001, Vol. 2, No. 3, pp. 177–186.
Maglione P., Parente C., Vallario A. Pan-sharpening Worldview-2: IHS, Brovey and Zhang methods in comparison, Int. J. Eng. Technol, 2016, No. 8, pp. 673–679.
Maglione P., Parente C., Vallario A. High resolution satellite images to reconstruct recent evolution of domitian coastline, American Journal of Applied Sciences, 2015, Vol. 12(7), pp. 506–515. DOI: 10.3844/ajassp.2015.506.515.
Xu Li, Mingyi He, Lei Zhang Hyperspherical color transform based pansharpening method for WorldView-2 satellite images, Proceedings of the 8th IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia, 2013, pp. 520–523. DOI: 10.1109/ICIEA.2013.6566424.
Aishwarya N., Abirami S., Amutha R. Multifocus image fusion using Discrete Wavelet Transform and Sparse Representation, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai, 2016, pp. 2377–2382. DOI: 10.1109/WiSP NET.2016.756656.
Chen S., Zhang R., Su H., Tian J., Xia J.Scaling-up transformation of multisensor images with multiple resolutions, Sensors, 2009, pp. 1370–1381.
Rouse J. W., Haas R. H., Schell J. A., Deering D.W. Monitoring Vegetation Systems in the Great Plains with ERTS, 3rd ERTS Symposium, NASA SP-351, Washington DC, 10– 14 December, 1973, pp. 309–317.
Mustafa T., Hassoon I. K., Hassan M., Modher H. Using Water Indices (ndwi, mndwi, ndmi, wri and awei) to Detect Physical and Chemical Parameters by Apply Remote Sensing and GIS Techniques, International Journal of Research, GRANTHAALAYAH, 2017, Vol. 5, I. 10, pp. 117–128. DOI: 10.5281/zenodo.1040209.
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