RUI: Collaborative Research: CDS&E: A Modular Multilayer Framework for Real-Time Hyperspectral Image Segmentation

RUI：合作研究：CDS

基本信息

批准号：
2003887
负责人：
Luis Cueva Parra
金额：
$ 12.06万
依托单位：
University of North Georgia
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003887&HistoricalAwards=false
关键词：
RUI Collaborative Research CDS&amp Modular

项目摘要

The analysis of images has been used by the scientific community to solve challenging problems and to get insight into diverse natural, social, and technical phenomena. Different types of images have been employed in various areas of study. One example is the hyperspectral images, which have higher resolution when compared to conventional camera images. Analyzing such images has its challenges. For instance, it is computationally demanding, and traditional methods have some limitations. This project provides an efficient solution to analyze such images, by exploiting high-performance computing tools and machine learning techniques. The resulting methods are applied to image-based atmospheric cloud detection.The project develops a real time, multi-layer, and modular segmentation framework for hyperspectral images. The developed framework automatically identifies various regions within a hyperspectral image by classifying each pixel of the image and associating them to class segments. The developed system is multi-layer, where each layer’s responsibility is to perform an operation on its input, generate region classification data, and pass the resultant output to the next layer. Importantly, each layer analyzes its input from distinct viewpoints, utilizing spectral and spatial data, resulting in a multi-layer framework where the layers complement each other. Also, this project aims to provide an optimized high-performance (speed-up and accuracy) computational tool for real-time hyperspectral image analysis. This is achieved by adapting the algorithms used in the different parts of the model for parallel processing.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.

科学界已经使用了对图像的分析来解决挑战问题，并深入了解潜水员的自然，社会和技术现象。在各个研究领域都采用了不同类型的图像。一个例子是高光谱图像，与常规摄像机图像相比，它们具有更高的分辨率。分析此类图像的挑战。例如，它是计算要求的，传统方法有一些局限性。该项目通过利用高性能计算工具和机器学习技术来提供有效的解决方案来分析此类图像。所得的方法应用于基于图像的大气云检测。该项目为高光谱图像开发了实时，多层和模块化分割框架。开发的框架通过对图像的每个像素进行分类并将它们关联到类段来自动识别高光谱图像中的各个区域。开发的系统是多层的，其中每层的责任是在其输入上执行操作，生成区域分类数据，然后将结果输出传递到下一层。重要的是，每层从不同的角度分析其输入，利用光谱和空间数据，从而产生了一个多层框架，其中图层相互补充。同样，该项目旨在提供优化的高性能（加速和准确性）计算工具，以实时高光谱图像分析。这是通过调整模型不同部分中用于并行处理的算法来实现的。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来审查标准，被认为是珍贵的支持。