Remote Sensing Image Fusion Based on Low-Level Visual Features and PAPCNN in NSST Domain

  Objectives  In order to solve the problems of the singleness of activity metric construction in fusion rules and the subjectivity of parameter setting of pulse-coupled neural network (PCNN), a remote sensing image fusion method combining low-level visual features and parameter adaptive pulse coupled neural network (PAPCNN) in the non-subsampled shearlet transform (NSST) domain is proposed in this paper.

  Methods  First, the panchromatic image and the luminance component Y in YUV color space of multispectral image are decomposed by NSST to obtain the high and low frequency components. Second, a fusion rule based on low-level visual features is used to low-frequency components fusion, and a new activity measure is constructed by combining three low-level features, namely, local phase congruency, local abrupt measure and local energy information. Then, PAPCNN model is used to high-frequency components fusion, and the multi-scale morphological gradient is used as the external input signal of the model. Finally, the fused image is obtained through NSST inverse transform and YUV inverse transform in turn.

  Results  The experimental results show that this method has better performance in remote sensing images of different platforms and different ground features. Compared with other 11 methods, this method has absolute advantages in all evaluation indexes.

  Conclusions  The proposed method can better preserve the spatial and spectral information in the original image, thus it can provide a fused image with complementary advantages.