# High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network

arXiv: https://arxiv.org/abs/2010.00472 · HTML: https://mlsanity.com/paper/2010.00472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing](https://mlsanity.com/paper/2404.18924/) (2024)
- [BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model](https://mlsanity.com/paper/2509.05895/) (2025)
- [Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution](https://mlsanity.com/paper/2202.10753/) (2022)
- [SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation](https://mlsanity.com/paper/2605.02198/) (2026)
- [Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks](https://mlsanity.com/paper/2605.13566/) (2026)

## Abstract (arXiv)

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.