Panchromatic loss-based spectral-spatial dual injection network for extreme pansharpening

Pansharpening involves synthesizing high resolution hyperspectral images (HRHS) by combining low spatial resolution hyperspectral (LRHS) images and panchromatic (PAN) images. Existing methods perform inadequately in extreme panchromatic sharpening, often resulting in excessively blurred HRHS images. The main reasons are the combination of inputs and training loss functions overly simplistic, and the spatial details of upsampling LRHS are severely distorted, which leads to the weakening of the neural network's ability to utilize the spatial information of PAN images. To address this issue, we propose a spectral-spatial dual injection network (SSDINet) combined with panchromatic loss for extreme pansharpening. SSDINet alleviates the blur problem of HRHS images during extreme pansharpening by adding an additional pseudo hyperspectral (PHS) image input and combining it with upsampling LRHS images to form an additional spectral injection branch that is different from spatial injection. Additionally, during network training, use an extra panchromatic loss to alleviates the problem of incomplete utilization of PAN images. Panchromatic mapping is realized by neural network. Experimental results demonstrate the superior performance of our approach compared to representative methods.