Paper Info
Title
Spatial Dynamic Selection Network for Remote-Sensing Image Fusion
Abstract
Nowadays, high-resolution images with rich spectral information are necessary for earth observation. Remote-sensing image fusion is an effective method to provide high-resolution multispectral images, which are obtained by fusing high-resolution panchromatic images and low-resolution multispectral images. However, existing methods mostly use the same network for image feature extraction, without considering the differences among different pixels, resulting in that the extracted features are not accurate enough. This letter proposes a spatial dynamic selection network for remote-sensing image fusion. A dynamic feature extraction module composed of multiple spatial dynamic blocks (SDBs) and cross-scale context connection blocks (CSCBs) is designed. The SDB can extract image features according to the input by different networks, and realize dynamic selection of pixel features. Since the spatial structure and spectral characteristic of each pixel are different, two complementary branches are designed in the SDB to extract different features, which improves the capability of feature extraction. Multiscale network structure is designed to obtain more abundant information and the CSCB is used to integrate the information of different scales. Experimental results on GeoEye-1 and WorldView-3 datasets demonstrate the superiority of the proposed method.
Year
DOI
Venue
2022
10.1109/LGRS.2021.3085140
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Feature extraction, Convolution, Remote sensing, Image fusion, Logic gates, Training, Robots, Convolutional neural network, cross-scale context connection, remote-sensing image fusion, spatial dynamic selection
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jianwen Hu100.34
Pei Hu200.34
Zeping Wang300.34
Xudong Kang445122.68
Shaosheng Fan500.34
Dun Mao600.34