Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening

Abstract

Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are generated by applying transform operation to the features of LRMS images and PAN images, which preserve the spatial location information. Then, the dynamic routing algorithm is modified to adaptively select the lower-level capsules to generate the higher-level capsule features to represent the fusion of LRMS images and PAN images, which can effectively avoid the loss of detailed information. In addition, the previous addition and concatenation operations are illustrated as special cases of our MDRL. Based on MIPSM with addition operations and DRPNN with concatenation operations, two modified dynamic routing models named MDR–MIPSM and MDR–DRPNN are further proposed for pan-sharpening. Extensive experimental results demonstrate that the proposed method can achieve remarkable spectral and spatial quality.