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.

中文翻译：

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用于改善农业热红外遥感的计算端到端和超分辨率方法

日益增加的全球缺水和提高产量的需求要求对灌溉、作物水分胁迫和作物总体状况进行高分辨率监测。为了提供高空间分辨率和高温精度，使用辐射长波热红外相机在低空进行遥感。然而，辐射相机的价格以及低海拔导致在给定时间内覆盖率低，限制了将辐射航空测量用于农业需求。本文介绍了使用算法和计算成像方法解决这两个限制的进展：稳定低成本热像仪的读数以获得辐射数据，并通过应用基于卷积神经网络的超分辨率来改善后者的低分辨率。这两种方法通过端到端算法管道合并，提供了该领域的大型镶嵌图像。首先，在遥感农业数据上模拟了联合估计方法纠正未知偏移和增益的潜在能力。与地面实况测量结果的比较显示辐射测量精度，均方根误差 (RMSE) 为 1.3 °C 至 1.8 °C。然后，在实验和模拟遥感农业数据上证明了所提出的超分辨率方法。初步实验结果表明，相对于双三次插值，图像锐度提高了 50%。该算法的性能在 22 个模拟案例上进行了评估，放大倍数为 2 倍和 4 倍。最后，演示了使用建议的管道进行图像镶嵌。