# High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Source: [High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:45:52.748Z Authors: Wenjia Xu, Guangluan Xu, Yang Wang, Xian Sun, Daoyu Lin, Yirong Wu Published: 2020-10-01T15:06:02.000Z ## Abstract Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and global memory connections to combine image detail with environmental information. To further reduce parameters and ease time-consuming, we propose downsampling units, shrinking the spatial size of feature maps. We test DMCN on three remote sensing datasets with different spatial resolution. Experimental results indicate that our method yields promising improvements in both accuracy and visual performance over the current state-of-the-art. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()