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Open AccessArticle

TS2uRF: A New Method for Sharpening Thermal Infrared Satellite Imagery

1
Faculty of Agricultural Engineering, University of Concepción, Chillán Casilla 537, Chile
2
Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28233 Pozuelo de Alarcón, Spain
3
School of Computer Engineering, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660 Boadilla del Monte, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 249; https://doi.org/10.3390/rs10020249
Received: 4 December 2017 / Revised: 28 January 2018 / Accepted: 30 January 2018 / Published: 6 February 2018
(This article belongs to the Section Remote Sensing Image Processing)
Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform. TIR resolution is often not suitable for monitoring crop conditions of fragmented farming lands, e.g., the accurate estimates of evapotranspiration (ET) based on surface energy balance from remote sensing for irrigation water management. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to a shortwave band pixel resolution. However, most methods concentrate on the visual effects of the thermal sharpened images, and they treat the pixels as independent samples without considering their spatial context, which can give rise to adverse effects such as artifacts. In this work, a new thermal sharpening method called TS2uRF is proposed. The potential of superpixels (SP) combined with regression random forest (RRF) have been used to augment the spatial resolution of the Landsat 8 TIR (100 m) imagery to their visible (VIS) spatial resolution (30 m). The SP has allowed the contextual information on the land cover to be integrated, and RRF has allowed the relationship between five spectral indices and TIR data to be integrated into a single model. The TIR sharpened images obtained using the TS2uRF were compared with images obtained using the TsHARP, one of the most classic thermal sharpening techniques, evaluating the root-mean-square error (RMSE) and structural similarity index (SSIM) for measuring image quality. In all of the cases evaluated, the RMSE and SSIM of the images sharpened using the TS2uRF method outperform those obtained using TsHARP. In particular, the TS2uRF method has an average error of 1.14 °C (RMSE) lower than TsHARP, regarding SSIM, TS2uRF outperforms TsHARP on average by 0.218 . From the visual comparison, it has been shown that the TS2uRF methodology avoids the artifacts that appear in the enhanced images using the TsHARP method. View Full-Text
Keywords: brightness temperature; thermal sharpening; superpixels; regression random forest brightness temperature; thermal sharpening; superpixels; regression random forest
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MDPI and ACS Style

Lillo-Saavedra, M.; García-Pedrero, A.; Merino, G.; Gonzalo-Martín, C. TS2uRF: A New Method for Sharpening Thermal Infrared Satellite Imagery. Remote Sens. 2018, 10, 249.

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