SBIR Phase II: Trident Bathymetry Mapping System: A Three-Pronged Automated Solution to Satellite Derived Shallow Seafloor Surveying

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力将是解决对全球生态和经济至关重要的浅水区或滨海区缺乏先进地图的问题。据估计，世界上70%的沿海地区没有按照现代标准绘制地图，传统的绘制这些区域的方法昂贵且危险，令人望而却步。通过遥感技术，这项创新将以比传统方法低得多的成本为全球浅水地区提供详细的水深数据。这对贫困、偏远、地势低洼的岛屿来说尤其重要，因为这些岛屿的资源稀缺，无法绘制其经济严重依赖的自然环境图。此外，浅水测深数据对于两栖登陆的地理空间情报和准确评估与全球贸易/港口发展、水产养殖、旅游和资源开发有关的项目的环境影响至关重要。由此产生的地理空间产品将为未来的科学分析、变化检测和更好地了解海洋环境提供现代海底地形基线。拟议的项目通过提出三种不同的计算卫星测深的方法来应对近岸水深测绘的挑战。这些方法依靠不同的测量物理参数进行水深反演，形成互补的测量结果。提取方法利用来自多个重叠图像的水下立体摄影测量、来自多光谱图像的波浪运动检测以及测量通过水柱的多光谱信号的可变衰减。将开展下列方面的研究：根据元海洋和大气参数评估和分配图像的计算机视觉应用；海底光谱分割技术及其对随后的多光谱深度反演的影响；立体摄影测量海底目标识别和关联的自动化；将海浪运动学得出的深度值与多光谱深度反演值进行整合和自动化。由于环境的复杂性和进入未知水域的航行危险，浅海底是一个动态的、知之甚少的领域。该项目的目标是通过实施人工智能、先进的遥感和计算机科学算法以及持续收集地球观测图像来创建一个远程产生的、自动的、自我验证的基于卫星的浅海测绘系统，以在全球范围内产生10-30米分辨率的测深高程模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to address the lack of advanced maps of shallow water zones, or littoral zones, crucial to global ecology and economy. An estimated 70% of the world's littoral zone is not mapped to modern standards, and traditional methods of mapping these areas are prohibitively expensive and dangerous. Through remote sensing techniques, this innovation will produce detailed bathymetric data for shallow water areas around the globe at far lower costs than traditional methods. This is particularly significant for impoverished remote, low-lying islands with scarce resources for mapping the natural environment on which their economies heavily rely. Additionally, shallow water bathymetry data is vital to geospatial intelligence for amphibious landings and accurate assessment of the environmental impact of projects relating to global trade/port development, aquaculture, tourism, and resource development. The resulting geospatial products will provide a modern baseline of seabed topography for future scientific analysis, change detection, and greater understanding of the marine environment.The proposed project addresses the challenges of nearshore bathymetry mapping by advancing three distinct methods of calculating satellite-derived bathymetry. These methods rely on different physical parameters of measurement for water depth retrieval to form complementary measurements. Extraction methods utilize underwater stereophotogrammetry from multiple overlapping images, wave kinematic detection from multispectral imagery, and measurement of variable attenuation of multispectral signals through the water column. Research will be conducted on computer vision applications for assessing and allocating images based upon metaocean and atmospheric parameters; seafloor spectral segmentation techniques and effects on subsequent multispectral depth retrieval; automation of stereophotogrammetric seafloor target identification and correlation; and integration and automation of wave kinematic derived depth values with multispectral depth retrieval values. The shallow seafloor is a dynamic and poorly understood domain due to the complexity of the environment and navigational dangers of accessing unknown waters. The project's goal is to create a remotely produced, automated, self-validating satellite-based shallow seafloor mapping system by implementing artificial intelligence, advanced remote sensing and computer science algorithms and ongoing collection of earth observation imagery to globally produce 10-30 m resolution bathymetry elevation models.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.