Next Article in Journal
Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements
Previous Article in Journal
Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia
Open AccessArticle

Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks

1
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
2
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, 10129 Turin, Italy
3
[email protected], Big Data and Data Science Laboratory, 10129 Turin, Italy
4
Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2207; https://doi.org/10.3390/rs12142207
Received: 20 June 2020 / Revised: 2 July 2020 / Accepted: 6 July 2020 / Published: 10 July 2020
Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature focused on the Single-image Super-resolution problem so far. At present, satellite-based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the Multi-image Super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for Single or Multi-image Super-resolution, demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-image Super-resolution for remote sensing applications. View Full-Text
Keywords: deep learning; multi-image super-resolution; attention networks; 3D convolutional neural networks deep learning; multi-image super-resolution; attention networks; 3D convolutional neural networks
Show Figures

Graphical abstract

MDPI and ACS Style

Salvetti, F.; Mazzia, V.; Khaliq, A.; Chiaberge, M. Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks. Remote Sens. 2020, 12, 2207. https://doi.org/10.3390/rs12142207

AMA Style

Salvetti F, Mazzia V, Khaliq A, Chiaberge M. Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks. Remote Sensing. 2020; 12(14):2207. https://doi.org/10.3390/rs12142207

Chicago/Turabian Style

Salvetti, Francesco; Mazzia, Vittorio; Khaliq, Aleem; Chiaberge, Marcello. 2020. "Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks" Remote Sens. 12, no. 14: 2207. https://doi.org/10.3390/rs12142207

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop