DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images

Since remote sensing images cannot have both high temporal resolution and high spatial resolution, spatiotemporal fusion of remote sensing images has attracted increasing attention in recent years. Additionally, with the successful application of deep learning in various fields, spatiotemporal fusio...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2020-10, Vol.20 (20), p.12190-12202
Main Authors: Li, Weisheng, Zhang, Xiayan, Peng, Yidong, Dong, Meilin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Artificial satellites
Computer architecture
Convolution
convolutional neural network
Deep learning
dilated convolution
Earth
Feature extraction
Feature maps
Machine learning
multiscale mechanism
Prediction algorithms
Remote sensing
skip connection
Spatial resolution
Spatiotemporal fusion
Spatiotemporal phenomena
Temporal resolution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Since remote sensing images cannot have both high temporal resolution and high spatial resolution, spatiotemporal fusion of remote sensing images has attracted increasing attention in recent years. Additionally, with the successful application of deep learning in various fields, spatiotemporal fusion algorithms based on deep learning have also gradually diversified. We propose a network framework that is based on deep convolutional neural networks that incorporate dilated convolution and multiscale mechanisms, we refer to this network framework as DMNet. In this method, we concatenate the feature maps that need to be fused to avoid using complex fusion methods to introduce noise. Then, the multiscale mechanism can extract the context information of the image at various scales, and make the image details more abundant. By adding skip connections, feature maps in shallow convolutional layers can be obtained to avoid losing important features of the image during the convolution. Additionally, dilated convolution expands the receptive field of the convolution kernel, which is conducive to the extraction of small detail features. To evaluate the robustness of our method, we conduct experiments on two datasets and compare the results with those obtained by six representative spatiotemporal fusion methods. Both intuitive and objective results demonstrate the superior performance of our method.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3000249