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

该合同将提供一个综合系统的开发，该系统能够高效、智能地提取、分析和管理用于对环境敏感生态系统中大量土地覆盖进行分类的非常大的高光谱数据集。高光谱数据提供了前所未有的光谱分辨率，可以转化为遥感区域的优越特征，但由于数据量大，高维，标记数据少以及大量潜在的土地覆盖类型或类别，因此带来了重大挑战。利用谱相关性的新自适应特征空间约简方法，处理小训练集的半监督和主动学习方法，以及将为一个区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，正在解决这些挑战。与此同时，将建立一个知识库，帮助快速识别给定领域中最相关的特征/类，从而大大减少数据存储需求和处理时间。这个跨学科项目需要数据采集和处理/分析之间的紧密互动，并将为其他工程问题提供见解。遥感数据分析结果的可视化性质使其成为向一般民众介绍诸如全球变暖和灾害管理的影响等广泛关注的问题的有力方式。最后，知识转移机制将有助于快速调整现有解决方案，以解决一些不同但相关的问题，从而大大提高现有点解决方案在几个应用领域的效用。

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.