Institute of Computer Science, Osnabrück University, 49090 Osnabrück, Germany.
Sensors (Basel). 2021 Nov 2;21(21):7296. doi: 10.3390/s21217296.
A promising low-cost solution for monitoring spectral information, e.g., on agricultural fields, is that of wireless sensor networks. In contrast to remote sensing, these can achieve more continuous monitoring due to their long-term deployment and are less impacted by the atmosphere, making them a promising solution for the calibration of satellite data. In this paper, we explore an alternative approach for processing data from such a network. Hyperspectral sensors were found to be too complex for such a network. While previous work considered fusing the data from different multispectral sensors in order to derive hyperspectral data, we shift the assessment of the hyperspectral modeling in a separate preprocessing step based on machine learning. We then use the learned data as additional input while using identical multispectral sensors, further reducing the complexity of the sensors. Despite requiring careful parametrization, the approach delivers hyperspectral data of similar and in some cases even better quality.
对于监测光谱信息（例如农业领域），无线传感器网络是一种很有前途的低成本解决方案。与遥感相比，由于其长期部署，这些传感器可以实现更连续的监测，并且受大气影响较小，因此成为校准卫星数据的一种很有前途的解决方案。在本文中，我们探讨了处理此类网络数据的另一种方法。高光谱传感器对于这样的网络来说过于复杂。虽然之前的工作考虑融合来自不同多光谱传感器的数据以得出高光谱数据，但我们将高光谱建模的评估转移到基于机器学习的单独预处理步骤中。然后，我们在使用相同的多光谱传感器的同时，将学习到的数据作为附加输入，进一步降低传感器的复杂性。尽管需要仔细参数化，但该方法提供了类似甚至在某些情况下质量更好的高光谱数据。
