Implementation and comparison of spectral and spatial quality of pixel-based satellite image fusion methods

Document Type : Original Article

Authors

1 Department of Geomatics, School of Marand Engineering, University of Tabriz,

2 Department of Geomatics, Marand Faculty of Engineering, University of Tabriz, Tabriz, Iran,

Abstract

In this research, 15 conventional image fusion techniques were applied and compared in four different groups. In addition, 10 distinct spectral-spatial criteria were investigated in four different modes to assess the outcomes. In the first case, every concrete criterion was considered, and the results revealed that, with the exception of the RVS and Gramshmit methods, which preserve both spectral and spatial information, the rest of the mentioned methods, while preserving spatial information, fail to preserve spectral information. In the second scenario, the average of seven homogeneous spectral criteria in two categories—minimum value and maximum value—was considered. In the maximum spectral criterion, LMVM, Gramshmit, SVR, and Ehler methods achieved the best performance, and in the minimum spectral criterion, Ehler, SVR, SFIM, and IHS methods ranked highest. In the third condition, the spatial criteria revealed that the LMM, ISVR, Brovey, and PCA algorithms perform best in terms of spatial information preservation. They were assessed jointly in the fourth scenario, taking the average of the lowest and highest spectral-spatial requirements into account. The findings revealed that the maximum mode, according to the LMM, LMVM, RVS, Ehler, Gramshmit, and SVR techniques, and the minimum mode, according to the Ehler, SVR, SFIM, and IHS methods, had the best performance in maintaining the spectral-spatial information. Finally, the results revealed that the SVR, Ehler, and Gramshmit algorithms perform the best in terms of maintaining spectral-spatial information when compared to other methods, preserving around 80 to 95% of the information.

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  • Receive Date: 01 January 2023
  • Revise Date: 30 January 2023
  • Accept Date: 15 March 2023