A novel SAR and optical image fusion algorithm based on an improved SPCNN and phase congruency information. Issue 4 (16th February 2023)

• Record Type: Journal Article
• Authors: Fu, Yukai; Yang, Shuwen; Li, Yikun; Yan, Heng; Zheng, Yao
• Is Part Of: International journal of remote sensing. Volume 44:Issue 4(2023)
• Page Start: 1328
• Page End: 1347
• Publication Date: 2023-02-16
• Subjects: Images fusion -- SAR and optical images -- pulse-coupled neural networks -- phase congruency information; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2023.2179899
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The noise and significant spectral differences lead to severe spectral and spatial information distortions in the fusion result of SAR and optical images. We propose a fusion method based on phase congruency information and an improved, simplified pulse-coupled neural network (PC-SPCNN). The PC-SPCNN method builds the basic fusion framework based on the generalized intensity-hue-saturation transform (GIHS) and nonsubsampled contourlet transform (NSCT). When fusing low-frequency coefficients, a fusion method that couples phase congruency and gain injection is adopted to reduce the spectral distortion caused by nonlinear radiometric differences between images. Meanwhile, an improved, simplified pulse-coupled neural network model is used to fuse the high-frequency coefficients of SAR and optical images. Three groups of multi-source, multi-scale, and multi-scene remote sensing images are used to verify the feasibility of PC-SPCNN and compared with existing fusion algorithms. The results indicate that the PC-SPCNN is superior to existing algorithms in both visual effect and objective evaluation and has better fusion performance.