A Novel Remote Sensing Images Fusion Algorithm Combining Extended NSST and Modified PCNN

摘要

In order to more accurately realize fusion of remote sensing images, we propose a novel remote sensing images fusion algorithm combining extended non-subsampled shearlet transform (NSST) and modified pulse-coupled neural network (PCNN). Firstly, it makes histogram matching and intensity smoothing and filtering treatment on intensity component and full-color image of multi-spectral image. Secondly, such intensity component and full-color image are decomposed by extended NSST to get corresponding high-frequency and low-frequency coefficients. For low-frequency coefficients, fusion is made by sparse representation; for high-frequency coefficients, a modified pulse-coupled neural network (PCNN) strategy is put forward to process. Finally, the processed result is drawn by inverse transformation of the extended NSST and intensity-hue-saturation inverse transformation. The experimental results show that the proposed algorithm reserves as much spectral information as possible and improve spatial resolution; its visual effects and objective indexes are better than other classical fusion algorithms.