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Open AccessEditor’s ChoiceLetter

Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

1
Environment and Climate Change Canada, Landscape Science and Technology, Ottawa, ON K1A 0H3, Canada
2
Natural Resources Canada, Earth Sciences Sector, Canada Center for Remote Sensing, Ottawa, ON K1A 0E4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 394; https://doi.org/10.3390/rs10030394
Received: 24 December 2017 / Revised: 27 February 2018 / Accepted: 27 February 2018 / Published: 3 March 2018
Landsat is a fundamental data source for understanding historical change and its effect on environmental processes. In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, tundra, and cropland/woodland environments. The analysis sought to assess baseline performance and determine the capacity for spatial and temporal extension of the trained CNNs. This is not a data fusion approach and a high-resolution image is only needed to train the CNN. Results show improvement with the deeper network generally achieving better results. For spatial and temporal extension, the deep CNN performed the same or better than the shallow CNN, but at greater computational cost. Results for temporal extension were influenced by change potentiality reducing the performance difference between the shallow and deep CNN. Visual examination revealed sharper images regarding land cover boundaries, linear features, and within-cover textures. The results suggest that spatial enhancement of the Landsat archive is feasible, with optimal performance where CNNs can be trained and applied within the same spatial domain. Future research will assess the enhancement on time series and associated land cover applications. View Full-Text
Keywords: super resolution; convolution neural network; Landsat; Sentinel-2 super resolution; convolution neural network; Landsat; Sentinel-2
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MDPI and ACS Style

Pouliot, D.; Latifovic, R.; Pasher, J.; Duffe, J. Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training. Remote Sens. 2018, 10, 394.

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