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

SBR-9513889 该奖项资助的研究将测试人工神经网络（ANN）在检测遥感图像变化方面的实用性。 探测和监测地球表面状况变化的能力对于了解人类对环境的影响和评估发展的可持续性至关重要。 地面测量受到后勤限制的严重限制，特别是在研究和评估全球变化方面。 因此，遥感提供了经常评估大面积地表状况及其变化的可能性。 目前，在通过遥感数据探测变化方面取得最大成功的是发生剧烈变化的情况。 该奖项资助的研究的目标是测试ANN在评估不太剧烈的变化时的效用。 人工神经网络将允许显着改善变化检测的期望是基于两个因素：人工神经网络已被证明比传统的基于几何学的方法更有效的遥感图像的分类，和调查人员已经取得了一些成功，在初步的努力，使用人工神经网络以这种方式。 测试需要几个组成部分：探索替代人工神经网络架构，解释人工神经网络的内部结构，分析遥感数据中的变化信号，评估人工神经网络的性能相对于传统的方法进行变化检测，制定措施，解释人工神经网络输出信号量化变化，并估计训练域以外的训练人工神经网络的鲁棒性。 调查人员将使用包含遥感和地面测量数据的现有数据集进行所有这些测试，以便以最低成本快速取得进展。 该研究具有理论和应用意义。 它将导致从概念上更好地了解陆地表面变化所产生的遥感图像中所含的光谱和时间信号。 它应该有助于确定哪些分析方法更适合于衡量不同背景下的变化。 这项研究将导致更好地理解使用，如果神经网络在数据分析框架，从而提高适当的是人工神经网络在地理研究。

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.