A novel SAR and optical image fusion algorithm based on an improved SPCNN and phase congruency information

• Published in: International journal of remote sensing Vol. 44; no. 4; pp. 1328 - 1347
• Main Authors: Fu, Yukai, Yang, Shuwen, Li, Yikun, Yan, Heng, Zheng, Yao
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 16.02.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Coefficients; Computer vision; Images fusion; Methods; Multisensor fusion; Neural networks; phase congruency information; pulse-coupled neural networks; Remote sensing; SAR and optical images; Saturation; Saturation (color); Spatial data; Visual effects
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2023.2179899

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.