Computational end-to-end and super-resolution methods to improve thermal infrared remote sensing for agriculture
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Computational end‑to‑end and super‑resolution methods to improve thermal infrared remote sensing for agriculture Iftach Klapp1 · Peretz Yafin1,2 · Navot Oz1,3 · Omri Brand1,4 · Idan Bahat1,5 · Eitan Goldshtein1 · Yafit Cohen1 · Victor Alchanatis1 · Nir Sochen4
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Increasing global water deficit and demand for yield improvement call for high-resolution monitoring of irrigation, crop water stress, and crops’ general condition. To provide high spatial resolution with high-temperature accuracy, remote sensing is conducted at low altitudes using radiometric longwave thermal infrared cameras. However, the radiometric cameras’ price, and the low altitude leading to low coverage in a given time, limit the use of radiometric aerial surveys for agricultural needs. This paper presents progress toward solving both limitations using algorithmic and computational imaging methods: stabilizing the readout of low-cost thermal cameras to obtain radiometric data, and improving the latter’s low resolution by applying convolutional neural network-based super-resolution. The two methods were merged by an end-to-end algorithm pipeline, providing a large mosaicked image of the field. First, the potential capabilities of a joint estimation method to correct unknown offset and gain were simulated on remotely sensed agricultural data. Comparison to ground-truth measurements showed radiometric accuracy with a root mean square error (RMSE) of 1.3 °C to 1.8 °C. Then, the proposed super-resolution method was demonstrated on experimental and simulated remotely sensed agricultural data. Preliminary experimental results showed 50% improvement in image sharpness relative to bicubic interpolation. The performance of the algorithm was evaluated on 22 simulated cases at × 2 and × 4 magnification. Finally, image mosaicking using the proposed pipeline was demonstrated. A mosaicked image composed of sub-images pre-processed by the proposed computational methods resulted in a RMSE in temperature of 0.8 °C, as compared to 8.2 °C without the initial processing. Keywords Computational imaging · Precision agriculture · Radiometry · Remote sensing and sensor · Super-resolution · Thermography
* Iftach Klapp [email protected] Extended author information available on the last page of the article
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Precision Agriculture
Introduction Remote sensing in the visible and near-infrared spectral range relies on solar radiation as the illumination source. The sun may be approximated to a black body (BB) at 5 900 degrees Kelvin (K) that illuminates in the range of 0.2 to 3 µm (Kopeika 1998). Wien’s law, which ties the peak radiation wavelength to BB radiation (Holman 1989), suggests that 𝜆max × Tbb = 2897.6 𝜇m × K where Tbb is the equivalent BB temperature (K) and λmax is the wavelength of the maximal radiant flux (μm). An ~ 300 K BB radiation can typically approximate plant radiation emission, and thus it radiates mostly at 10 μm (infrared radiation, IR). Em
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