The invention discloses a multi-
hyperspectral image fusion method guided by low-rank prior and
spatial spectrum information, and provides a brand new multi-layer multi-
branch fusion network SSLRNet combining
spatial spectrum guidance and low-rank prior, the network firstly constructs a multi-layer multi-
branch fusion sub-network (MLMB), and aims to extract features from a plurality of branches, and then the multi-layer multi-
branch fusion sub-network SSLRNet constructs a multi-layer multi-branch fusion sub-network SSLRNet; and multi-layer
feature fusion is carried out to reconstruct a preliminary
fusion image. And then, constructing a
fusion image spatial spectrum correction sub-network based on spatial spectrum guidance, and performing spatial spectrum guidance on a preliminary
fusion image generated by the MLMB through a
multispectral image waveband superposition summation image and a hyperspectral image waveband average value image, thereby reducing spatial spectrum
distortion. And finally, constructing a fusion image low-rank prior constraint sub-network based on a low-rank neural network, combining the sub-network with a
deep learning network, and performing low-rank
decomposition by utilizing the characteristics of the network, so that a fusion result better meets a real application requirement. According to the invention, the fusion precision of the network is improved, and real application requirements are better met.