An Algorithm Based on PCGP Image Fusion for Multi-Source Remote Sensing Images.

Heterogeneous images imaged by different types of sensors have different imaging mechanisms, reflecting the characteristics of different sides of the target scene; while multi-source images formed by different working platforms or at different times have different imaging perspectives, and provide different target scene information. The use of multi-source heterogeneous images for fusion to obtain target and scene information more accurately and comprehensively has potential important applications in many fields such as military, medicine, and meteorology, and has become an important branch of image processing research. To this end, a PCGP algorithm is proposed in this paper to realize the fusion of optical images from different sources and SAR images. It first applies PCA transformation to the multi-source data images to obtain the principal component variables, then performs histogram matching on the first principal components of the transformed data sources, and finally uses the gradient pyramid decomposition algorithm to fuse the matched images to obtain a fused image. Then, the proposed fusion algorithm is tested in the fusion task of remote sensing images from different sources of GF2, GF6 and GF3. The experimental results show that the proposed fusion algorithm has better results.