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

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.