EAGER: IMPRESS-U: Exploratory Research on Generative Compression for Compressive Lidar

该IMPRESS-U项目由NSF、波兰国家科学中心、美国国家科学院和海军全球研究办公室（DoD）共同资助。这项研究将在一个多边国际合作伙伴关系中进行，该合作伙伴关系将美国特拉华州大学、乌克兰哈尔科夫国家航空航天大学和波兰斯切钦的西波美拉尼亚理工大学联合起来。美国部分的合作将由国际科学与工程办公室（OISE），刺激竞争研究的既定计划（EPSCoR）以及通信和信息基础计划（CCF）共同资助。 拟议的努力旨在：（a）在来自乌克兰、波兰和美国的学术研究团队之间建立伙伴关系;（B）在乌克兰建立一个具有弹性的协作研究和卓越教育计划，用于卫星激光雷达对地球的感知;（c）探索生成压缩的全新概念，用于压缩激光雷达测量的存储或通信。这些方法将应用于从新一代卫星激光雷达，即压缩激光雷达（CS激光雷达）获得的数据，这些激光雷达将用于揭示地球表面及其森林的拓扑结构，这些结构对生态系统过程具有深远的影响。目前的星载激光雷达在空间分辨率和覆盖范围方面受到限制，因为激光反射仅沿沿着1D足迹线扫描测量。压缩激光雷达从地球上空数百公里处对地球进行稀疏测量，然后通过计算重建具有分辨率和覆盖范围的3D图像，就好像数据是从数百米的高度收集的一样。迄今为止，卫星激光雷达不使用数据压缩来避免信息丢失。然而，CS激光雷达依赖于深度学习重建算法，覆盖地球上更大数量级的区域，在这些区域中自然会出现数据压缩的机会。因此，该项目将探索使用生成模型进行数据压缩的全新方法，这些模型已显示出在更深的压缩水平上产生更准确的图像重建的潜力。该项目将在乌克兰和波兰招募一批新的人才，他们将受到激励，开始围绕机器学习和地球遥感交叉的持久职业生涯。星载激光雷达是一种重要的成像技术，用于揭示地球表面及其森林的拓扑结构，这些拓扑结构对决定地球上营养、水和碳循环的生态系统过程产生深远影响。目前的星载激光雷达在空间分辨率和覆盖范围方面受到限制，因为激光反射仅沿沿着1D足迹线扫描测量。在这两者之间，大量的景观仍然没有采样照明。为了克服这一限制，NASA正在开发新一代卫星激光雷达，采用全新的传感模式，传统的一维线扫描被抛弃，取而代之的是稀疏和宽视场激光雷达照明，即压缩卫星（CS）激光雷达。其目标是从地球上空数百公里处对地球进行压缩感知，然后通过计算重建具有分辨率和覆盖范围的3D图像，就好像数据是从数百米的高度收集的一样。迄今为止，NASA的卫星激光雷达没有使用测量数据压缩来避免信息丢失。然而，CS激光雷达依赖于深度学习重建算法，覆盖地球上更大数量级的区域，这些区域自然会出现数据压缩的机会。NASA的CS激光雷达团队目前还没有探索这个问题，因此提议的探索性研究工作是有价值的，互补的，及时的。虽然传统的图像压缩依赖于手工制作的编码器/解码器对，但研究团队将探索使用生成模型进行数据压缩的全新方法，这些模型已显示出在更深的压缩级别上产生更准确图像重建的潜力。CS激光雷达有望显著提高卫星测高的视场和成像分辨率，其中数据压缩变得越来越重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This IMPRESS-U project is jointly funded by NSF, National Science Center of Poland, US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites the University of Delaware, US, the National Aerospace University in Kharkiv, Ukraine, and the West Pomeranian University of Technology in Szczecin, Poland. US portion of the collaborative effort will be co-funded by Office of International Science and Engineering (OISE), Established Program to Stimulate Competitive Research (EPSCoR), and Communications and Information Foundations Program (CCF). The proposed effort aims at: (a) establishing a partnership among academic research teams from Ukraine, Poland, and the US; (b) building a resilient and collaborative research and education program of excellence in Ukraine in machine learning (ML) for satellite lidar sensing of Earth; and (c) exploring radically new concepts in generative compression for the storage or communication of compressive lidar measurements. The methods will be applied to data obtained from a new generation of satellite lidars, coined compressive lidars (CS lidar), that will be used to unravel the topological structure of the Earth’s surface and its forests, which have a profound effect on ecosystem processes. Spaceborne lidars today are limited in spatial resolution and coverage since laser reflections are only measured along 1D footprint line scans. Compressive lidars take sparse measurements of Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage, as if the data was collected from just hundreds of meters in height. To date, satellite lidars do not use data compression to avoid loss of information. CS lidars, however, rely on deep learning reconstruction algorithms covering orders of magnitude larger areas of Earth where the opportunity of data compression arises naturally. This project will thus explore radically new approaches to data compression using generative models which have shown the potential to produce more accurate image reconstructions at much deeper compression levels. The project will recruit a fresh cohort of talent in Ukraine and Poland who will be galvanized to embark on enduring careers that revolve around the intersection of machine learning and Earth remote sensing. Spaceborne lidar is an important imaging technology that is used to unravel the topological structure of the Earth’s surface and its forests which have a profound effect on ecosystem processes that determine nutrient, water, and carbon cycles on Earth. Spaceborne lidars today are limited in spatial resolution and coverage since laser reflections are only measured along 1D footprint line scans. In between these, vast amounts of landscape remain without sampling illumination. To overcome this limitation, a new generation of satellite lidars are being developed at NASA taking on a radically new sensing paradigm where the traditional 1D line scanning is abandoned and replaced by sparse and wide-field-of view lidar illumination, coined compressive satellite (CS) lidars. The objective is to compressively sense Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage, as if the data was collected from just hundreds of meters in height. NASA’s satellite lidars to date do not use data compression of the measurements to avoid loss of information. CS lidar, however, relies on deep learning reconstruction algorithms covering orders of magnitude larger areas of Earth where the opportunity of data compression arises naturally. NASA’s CS lidar team is currently not exploring this problem and thus the proposed exploratory research effort is valuable, complementary, and timely. While traditional image compression relies on hand-crafted encoder/decoder pairs, the research team will explore radically new approaches to data compression using generative models which have shown the potential to produce more accurate image reconstructions at much deeper compression levels. CS lidars promise to significantly enhance both the field-of-view and imaging resolution of satellite altimetry where data compression becomes increasingly important.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.