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Open AccessArticle

Sequence Image Interpolation via Separable Convolution Network

1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3
CNRS UMR 6554 LETG, Université de Rennes 2, Place du recteur Henri le Moal, 35000 Rennes, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 296; https://doi.org/10.3390/rs13020296
Received: 27 November 2020 / Revised: 8 January 2021 / Accepted: 14 January 2021 / Published: 15 January 2021
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information. View Full-Text
Keywords: sequence image interpolation; separable convolution network; separable convolution kernel; UAV dataset; Landsat-8 dataset sequence image interpolation; separable convolution network; separable convolution kernel; UAV dataset; Landsat-8 dataset
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MDPI and ACS Style

Jin, X.; Tang, P.; Houet, T.; Corpetti, T.; Alvarez-Vanhard, E.G.; Zhang, Z. Sequence Image Interpolation via Separable Convolution Network. Remote Sens. 2021, 13, 296. https://doi.org/10.3390/rs13020296

AMA Style

Jin X, Tang P, Houet T, Corpetti T, Alvarez-Vanhard EG, Zhang Z. Sequence Image Interpolation via Separable Convolution Network. Remote Sensing. 2021; 13(2):296. https://doi.org/10.3390/rs13020296

Chicago/Turabian Style

Jin, Xing; Tang, Ping; Houet, Thomas; Corpetti, Thomas; Alvarez-Vanhard, Emilien G.; Zhang, Zheng. 2021. "Sequence Image Interpolation via Separable Convolution Network" Remote Sens. 13, no. 2: 296. https://doi.org/10.3390/rs13020296

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