ATD: Threat Detection Problems in Precision Agriculture and Satellite Imaging

点击翻译按钮获取中文摘要

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.