Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

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Abstract
In this paper, a remote sensing image fusion method based on boundary measured dual-channel pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. Firstly, the panchromatic (PAN) image is decomposed into three parts, a small-scale image, a large-scale image, and a base image through a co-occurrence filtering (CoF)-based decomposition model. Secondly, an HSI transform is applied in the multispectral (MS) image to obtained intensity, hue and saturation components. Thirdly, a PCNN fusion strategy modulated by MSMG is used to fuse the base image and the intensity component of the MS image. Then, a fused intensity image is obtained by combining the small-scale image, large-scale image and the fused approximate image. Finally, the final fused image can be reconstructed by an inverse HSI transform. Experiments in four datasets demonstrate that the proposed method obtains the best performance in most cases.
Funding Information
  • National Natural Science Foundation of China (61675160)
  • Higher Education Discipline Innovation Project (B17035)
  • China Scholarship Council (CSC201906960047)

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