A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

Peng, Mingyuan; Zhang, Lifu; Sun, Xuejian; Cen, Yi; Zhao, Xiaoyang

doi:10.3390/rs12233888

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A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

by

Mingyuan Peng

^1,2,

Lifu Zhang

^1,*

,

Xuejian Sun

¹,

Yi Cen

¹ and

Xiaoyang Zhao

^1,2

¹

The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

²

University of Chinese Academy of Sciences, Beijing 100049, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(23), 3888; https://doi.org/10.3390/rs12233888

Submission received: 1 November 2020 / Revised: 25 November 2020 / Accepted: 26 November 2020 / Published: 27 November 2020 / Corrected: 14 June 2022

(This article belongs to the Special Issue Remote Sensing Data Fusion as a Strategy to Add Value to Earth Observation Data)

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Abstract

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

Keywords: data fusion; spatiotemporal fusion; spatial-temporal-spectral dataset; 3DCNN

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MDPI and ACS Style

Peng, M.; Zhang, L.; Sun, X.; Cen, Y.; Zhao, X. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888. https://doi.org/10.3390/rs12233888

AMA Style

Peng M, Zhang L, Sun X, Cen Y, Zhao X. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sensing. 2020; 12(23):3888. https://doi.org/10.3390/rs12233888

Chicago/Turabian Style

Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. 2020. "A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset" Remote Sensing 12, no. 23: 3888. https://doi.org/10.3390/rs12233888

APA Style

Peng, M., Zhang, L., Sun, X., Cen, Y., & Zhao, X. (2020). A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sensing, 12(23), 3888. https://doi.org/10.3390/rs12233888

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A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

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