D2TNet: A ConvLSTM Network With Dual-Direction Transfer for Pan-Sharpening

Meiqi Gong, Jiayi Ma, Han Xu, Xin Tian, Xiao-Ping Zhang

Abstract: In this article, we propose an efficient convolutional long short-term memory (ConvLSTM) network with dual-direction transfer for pan-sharpening, termed D2TNet. We design a specially structured ConvLSTM network that allows for dual-directional communication, including multiscale information and multilevel information. On the one hand, due to the sensitivity of spatial information to scales and the sensitivity of spectral information to levels, multiscale and multilevel information is extracted to facilitate the fuller use of source images. On the other hand, ConvLSTM is employed to capture the strong dependencies between multiscale information and multilevel information. Besides, we introduce a multiscale loss to enable different scales contributing to each other to generate high-resolution multispectral images that are closer to the ground truth. Extensive experiments, including qualitative evaluation, quantitative evaluation, and efficiency comparison, are implemented to verify that our D2TNet outperforms state-of-the-art methods indeed.