ITR: Extraction and Interpretation of Information from Large-Scale Hyperspectral Data for Mapping and Monitoring Wetland Ecosystems

ITR：从大规模高光谱数据中提取和解释信息，用于绘制和监测湿地生态系统

• 批准号: 0312471
• 负责人: Joydeep Ghosh
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312471&HistoricalAwards=false
• 关键词: ITR; Extraction; Interpretation; Information; Large

This award will provide for the development of a comprehensive system that can efficiently and intelligently extract, analyze and manage very large hyperspectral datasets used for classifying a large variety of land covers in environmentally sensitive ecosystems. Hyperspectral data provide unprecendented spectral resolution which can translate to far superior characterization of remotely sensed areas, but pose significant challenges because of the large data volumes, high dimensionality, little labelled data and large number of potential land cover types or classes. These challenges are being addressed by new adaptive feature space reduction methods that exploit spectral correlations, by semi-supervised and active learning methods for dealing with small training sets, and by knowledge reuse and transfer mechanisms that adapt models developed for one area to new regions with related characteristics. In parallel, a knowledge repository that helps rapidly identify the most pertinent features/classes for a given area, will be built to substantially reduce data storage requirements and processing time.This inter-disciplinary project requires tight interaction between data acquisition and processing/analysis, and will provide insights for other engineering problems as well. The visual nature of results from analysis of remotely sensed data make it a powerful modality of introducing the general population to issues of broad concern, such as the impact of global warming and disaster management. Finally, the knowledge transfer mechanisms will be useful for rapidly adapting existing solutions to somewhat different but related problems, thus substantially increasing the utility of existing point solutions in several application domains.

该奖项将用于开发一种全面的系统，该系统可以高效和智能地提取、分析和管理用于对环境敏感生态系统中的各种土地覆盖进行分类的超光谱数据集。高光谱数据提供了前所未有的光谱分辨率，这可以转化为对遥感区域的更好的表征，但由于数据量大、维度高、标签数据少以及大量潜在的土地覆盖类型或类别，高光谱数据构成了重大挑战。这些挑战正在通过利用光谱相关性的新的自适应特征空间约简方法、通过处理小训练集的半监督和主动学习方法以及通过使为一个领域开发的模型适应具有相关特征的新区域的知识重用和转移机制来解决。同时，将建立一个知识库，帮助快速确定特定地区最相关的特征/类别，以大大减少数据存储要求和处理时间。这一跨学科项目需要数据采集和处理/分析之间的密切互动，并将为其他工程问题提供见解。遥感数据分析结果的视觉特性使其成为向普通民众介绍诸如全球变暖和灾害管理等广泛关注的问题的一种强有力的方式。最后，知识转移机制将有助于快速调整现有解决方案，使其适应一些不同但相关的问题，从而大大增加现有点状解决方案在几个应用领域的效用。