SBIR Phase II: High Resolution, Synthetic Satellite Imagery of the Earth

SBIR 第二阶段：地球的高分辨率合成卫星图像

• 批准号: 1660067
• 负责人: Stephanie Hulina
• 金额: $ 74.82万
• 依托单位: Geospatial Data Analysis Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660067&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Resolution; Synthetic

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to address a strong commercial and scientific need for operational synthesis of spatially consistent and temporally relevant historical and current high resolution satellite imagery for analytical purposes. The technology will contribute to the advancement of scientific knowledge especially in the geospatial arena and to market spillovers. By dramatically simplifying access to accurate imagery for any time and place, this technology will provide companies, researchers, educators, students, and regular citizens with a valuable tool for visualizing and exploring our changing planet and will contribute to increasing public engagement with science and technology. Further, the analytical capabilities offered by the imagery have great potential in scientific applications thus contributing to partnerships between academia and industry and improving datasets for research and education. Finally, this technology will be valuable in operational settings at the large providers of commercial satellite imagery, to individual users, and enable a wide variety of new visualization, analysis, and data mining applications. The examples of commercial applications for the technology are virtual earth, insurance and reinsurance, agriculture, emergency, change detection.This Small Business Innovation Research (SBIR) Phase II project will operationally synthesize accurate regional-to-global, high spatial / high temporal satellite imagery of the Earth. The technology will utilize advanced data fusion algorithms to combine various sources of imagery while preserving the best spatial and temporal attributes of the data sources. The complexity of accessing, processing, and analyzing various sources of satellite imagery creates a significant barrier to its use. Synthesis of regionally and globally continuous high spatial / high temporal resolution imagery is a challenge as in addition to inherent differences in spatial and temporal resolutions of the source data, the new models need to account for enormous data volumes and sparse coverage of high spatial resolution imagery. Existing techniques to handle these challenges have severe limitations which curtail their use outside of the research arena. The technology will overcome these limitations by implementing algorithms that are robust, automated, scalable, deliver accurate data, and are usable in operational settings. They will provide spatially consistent and temporally relevant imagery which will empower businesses with regional and global outreach to make better decisions with better data.

这一小企业创新研究（SBIR）第二阶段项目的更广泛影响/商业潜力将是满足商业和科学上的强烈需求，即为分析目的对空间一致和时间相关的历史和当前高分辨率卫星图像进行业务合成。该技术将有助于科学知识的进步，特别是在地理空间竞技场领域，并有助于市场溢出效应。通过极大地简化在任何时间和地点获取准确图像的过程，这项技术将为公司、研究人员、教育工作者、学生和普通公民提供一个宝贵的工具，用于可视化和探索我们不断变化的地球，并将有助于增加公众对科学和技术的参与。此外，图像提供的分析能力在科学应用方面具有巨大潜力，从而有助于学术界和工业界之间的伙伴关系，并改进用于研究和教育的数据集。最后，这项技术将在商业卫星图像的大型提供商的操作环境中对个人用户有价值，并使各种新的可视化，分析和数据挖掘应用成为可能。该技术的商业应用的例子有虚拟地球、保险和再保险、农业、紧急情况、变化探测，这一小企业创新研究第二阶段项目将在业务上合成准确的区域到全球、高空间/高时间的地球卫星图像。该技术将利用先进的数据融合算法，将各种图像来源联合收割机结合起来，同时保留数据来源的最佳空间和时间属性。访问、处理和分析各种来源的卫星图像的复杂性对其使用造成了重大障碍。综合区域和全球连续的高空间/高时间分辨率图像是一项挑战，因为除了源数据在空间和时间分辨率方面的固有差异外，新模型还需要考虑到巨大的数据量和高空间分辨率图像的稀疏覆盖。处理这些挑战的现有技术具有严重的局限性，这限制了它们在研究竞技场之外的使用。该技术将通过实现强大的、自动化的、可扩展的、提供准确数据的、可在操作环境中使用的算法来克服这些限制。它们将提供空间一致和时间相关的图像，使企业能够在区域和全球范围内利用更好的数据做出更好的决策。