Sscnet: Spectral-Spatial Consistency Optimization Of Cnn For Pansharpening

Recently, convolutional neural network (CNN) has achieved great results in pansharpening. Most pansharpening methods with CNN are based on PNN [1] inspired by super-resolution methods with CNN and learn the pansharpening of downsampled images. In this work, we presented a novel framework for pansharpening based on two desired property of pansharpened images: downsampled pansharpened images become low-resolution multi-spectral images (spectral consistency) and panchromatic images are approximated by weighted addition of each bands of pansharpened images (spatial consistency). Our framework train CNN to learn this spectral-spatial consistency. The advantage of our framework is that there is no scale mismatch between training and test data. We applied our method to Landsat-8 images and compared it with some previous methods.