Next Article in Journal
Long-Term (1986–2015) Crop Water Use Characterization over the Upper Rio Grande Basin of United States and Mexico Using Landsat-Based Evapotranspiration
Previous Article in Journal
Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields
Previous Article in Special Issue
Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

1
Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2
School of Computer Science, Wuhan University, Wuhan 430072, China
3
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1588; https://doi.org/10.3390/rs11131588
Received: 13 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 4 July 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
  |  
PDF [3445 KB, uploaded 4 July 2019]
  |  

Abstract

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities. View Full-Text
Keywords: satellite imagery; super-resolution; residual network; multi-scale image; convolutional neural network satellite imagery; super-resolution; residual network; multi-scale image; convolutional neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lu, T.; Wang, J.; Zhang, Y.; Wang, Z.; Jiang, J. Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sens. 2019, 11, 1588.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top