Assessment of Landuse and Land Cover Change Using Remote Sensing and Artificial Neural Networks

利用遥感和人工神经网络评估土地利用和土地覆盖变化

• 批准号: 9513889
• 负责人: Sucharita Gopal
• 金额: $ 19.25万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 1999-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9513889&HistoricalAwards=false
• 关键词: Assessment; Landuse; Land; Cover; Change

SBR-9513889 The research funded by this award will test the utility of artificial neural networks (ANN) in detecting changes in remotely sensed images. The ability to detect and monitor changes in conditions at the Earth's surface is fundamental to an understanding of human impacts on the environment and to the assessment of the sustainability of development. Ground based measurements are severely limited by logistical constraints, particularly in the research and assessment of global change. Therefore, remote sensing provides the potential for frequent assessment of surface conditions and their change over large areas. Currently, the greatest success in change detection via remotely sensed data is for situations of dramatic change. The goal of the research funded by this award is to test the utility of ANN in assessing less dramatic change. The expectation that ANN will allow significant improvements in change detection is based on two factors: ANN have proven more effective than conventional statistics-based methods for classification of remote sensing imagery, and the investigators have had some success in a preliminary effort to use ANN in this way. The testing entails several components: exploring alternative ANN architectures, interpreting the internal structure of the ANN to analyze the changes signal in the remotely sensed data, evaluating the performance of the ANN relative to conventional methods for change detection, developing measures to interpret ANN output signals for quantifying change, and estimating the robustness of trained ANN outside of the training domain. The investigators will use existing data sets containing remotely sensed and ground measurements for all these tests, allowing rapid progress at minimal cost. The research has both theoretical and applied implications. It will lead to a greater conceptual understanding of the spectral and temporal signals contained in remotely sensed images resulting from land surface change. It should help determine what analytic methods are better suited to measuring change in different contexts. The research will lead to an improved understanding of the use if neural networks in a data-analytic framework, and thus enhance the appropriate is of ANN in geographic research.

SBR-9513889该奖项资助的研究将测试人工神经网络（ANN）在检测远程感知图像的变化方面的效用。 检测和监测地球表面状况变化的能力是了解人类对环境的影响以及评估发展可持续性的基础。 基于地面的测量受到后勤限制的严重限制，尤其是在全球变化的研究和评估中。 因此，遥感为经常评估表面条件及其在大面积的变化提供了潜力。 当前，通过远程感知的数据在变更检测中的最大成功是对于急剧变化的情况。 该奖项资助的研究的目的是测试ANN评估不太戏剧性变化的实用性。 对ANN将允许重大改进检测的期望是基于两个因素：ANN比传统的基于统计的方法对遥感图像进行分类更有效，并且研究人员在初步的努力中取得了一些成功，以这种方式使用ANN。 测试需要几个组成部分：探索替代的ANN体系结构，解释ANN的内部结构，以分析远程感知的数据中的变化信号，评估ANN相对于传统方法的变更检测方法的性能，开发用于量化ANN输出信号的措施，以量化量化的变化，并估算训练域外的训练有素的ANN的稳健性。 研究人员将使用包含所有这些测试的远程感知和地面测量的现有数据集，以最低的成本快速进步。 该研究既具有理论和应用的含义。 这将导致对由于土地表面变化而产生的远程感知图像中包含的光谱和时间信号的概念性理解。 它应该有助于确定哪些分析方法更适合于衡量不同情况下的变化。 这项研究将导致对数据分析框架中的神经网络的使用，从而提高对使用的了解，从而提高地理研究中的ANN。