ATD: Threat Detection Problems in Precision Agriculture and Satellite Imaging

该项目通过利用对精确农业和卫星成像中相关未解决问题的研究，探索可能的威胁检测技术。这些问题中的一些目前在实验室环境中得到解决，但在遥感的实际环境中没有令人满意的解决方案。除了安全方面的好处外，这项研究还可以节省种植者的资金，并通过只在需要的时候和地方使用所需的投入来减少农业对环境的影响。该项目对卫星成像和测量的贡献还将加强环境监测。高效率的遥感将能够从最少量的数据中准确地探测威胁，同时延长扫描仪的使用寿命并降低相关费用，该项目旨在开发必要的数据清理和处理的关键方法，以确保航空和卫星图像产生有意义的结果。例如，研究人员将探索一种仔细而新颖的方法来去除卫星图像中的云。有必要采用一种新的方法，因为测量植被指数有助于确定对农作物的各种威胁，但在云层造成阴影的情况下，这种方法变得不可靠。研究人员和合作者还将解决图像拼接的技术问题，或解决农业领域的大型难题。这里的困难在于，斑块彼此非常相似，有时几乎没有重叠。其他有待研究的技术问题是超分辨率和非均匀设置中的修复。一旦这些技术问题得到解决，研究人员计划调整和进一步开发威胁检测技术，特别是多变量变化点检测技术。农业中强大的威胁检测也将有助于优化作物产量，同时最大限度地减少资源。因此，在这些领域的威胁检测能力的进步，广泛影响社会，甚至超出其内在的用处。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project explores possible techniques for threat detection by leveraging research into related unsolved problems in precision agriculture and satellite imaging. Some of these problems are currently solved in a laboratory setting but do not have satisfying solutions in the practical setting of remote sensing. In addition to security benefits, the research has the potential to save growers money and reduce the environmental impact of agriculture by applying only the inputs that are needed only when and where they are needed. The project's contributions to satellite imaging and measurement would also enhance environmental monitoring. Efficient remote sensing would enable accurate threat detection from minimal amounts of data, while increasing the life span of scanners and reducing associated costs.This project aims to develop crucial methods for the data cleaning and processing necessary to ensure meaningful results in aerial and satellite imagery. For example, the investigators will explore a careful and novel approach to cloud removal in satellite images. A new approach is necessary since the measurement of vegetation indices, which help identify various threats to crops, becomes unreliable in the presence of shadows introduced by clouds. The investigator and collaborators will also address the technical problem of image stitching, or solving large puzzles, for agricultural fields. The difficulty here is that patches are very similar to each other and sometimes have very little overlap. Other technical problems to be studied are super-resolution and inpainting in non-uniform settings. Once these technical problems are solved, the investigators plan to adapt and further develop techniques for threat detection, in particular, techniques for multivariate change point detection. Robust threat detection in agriculture will also help to optimize crop yield while minimizing resources. Thus, advances in threat detection capabilities in these domains broadly impact society even beyond their intrinsic usefulness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.