Double-Stack Aggregation Network Using a Feature-Travel Strategy for Pansharpening

Pansharpening methods based on deep learning can obtain high-quality, high-resolution multispectral images and are gradually becoming an active research topic. To combine deep learning and remote sensing domain knowledge more efficiently, we propose a double-stack aggregation network using a feature...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 17; p. 4224
Main Authors Li, Weisheng, He, Maolin, Xiang, Minghao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Agglomeration
Algorithms
Computer applications
convolutional neural network
Deep learning
dense fusion
Design
double-stack aggregation
Feature extraction
feature-travel
Image quality
Image resolution
large kernel
Machine learning
Modules
Neural networks
Receptive field
Remote sensing
Resampling
Sensors
Travel
Wavelet transforms
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Summary:Pansharpening methods based on deep learning can obtain high-quality, high-resolution multispectral images and are gradually becoming an active research topic. To combine deep learning and remote sensing domain knowledge more efficiently, we propose a double-stack aggregation network using a feature-travel strategy for pansharpening. The proposed network comprises two important designs. First, we propose a double-stack feature aggregation module that can efficiently retain useful feature information by aggregating features extracted at different levels. The module introduces a new multiscale, large-kernel convolutional block in the feature extraction stage to maintain the overall computational power while expanding the receptive field and obtaining detailed feature information. We also introduce a feature-travel strategy to effectively complement feature details on multiple scales. By resampling the source images, we use three pairs of source images at various scales as the input to the network. The feature-travel strategy lets the extracted features loop through the three scales to supplement the effective feature details. Extensive experiments on three satellite datasets show that the proposed model achieves significant improvements in both spatial and spectral quality measurements compared to state-of-the-art methods.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14174224