Target-based multiple-scale change detection from time series remotely sensed environmental data

基于时间序列遥感环境数据的基于目标的多尺度变化检测

• 批准号: RGPIN-2019-05773
• 负责人: Chen, Dongmei
• 金额: $ 3.13万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738504
• 关键词: Target; based; multiple; scale; change

Remotely sensed (RS) data has been widely used in environmental monitoring and health applications. Linking the spectral and pattern changes among multi-temporal RS data sets to changes in surface features and biophysical variables has long been an important application of remote sensing technology. Although various change detection methods have been developed, they are mainly based on image analysis on multi-temporal images acquired from the same or similar sensor. With the rapid development of remote sensing technology and the increasing availability of large multi-scale and multi-sensor earth observation data sets, change detection has been forced to incorporate those data sets, and more efficient change detection methods and robust tools are needed to meet the challenge. This research is based on the innovative concept of target-based change detection we have proposed and is aimed to improve the accuracy of change detection by (i) evaluating the performance of different spectral unmixing and sub-pixel methods for mapping targeted land cover and its change across different scales; (ii) developing intelligent change detection procedures through data mining and deep learning from multi-spectral, multi-scale, multi-temporal RS data cubes for different targets on different landscapes; and (iii) developing an evaluation framework for hybrid change detection when multi-scale and multi-sensor temporal images are involved. The long-term goal of the proposed research is to provide theoretical frameworks and robust tools for better understanding the error and uncertainty in change detection and detecting patterns and changes from the large amount of multi-sensor time series RS images and products. The innovative change detection methods and evaluation framework from the proposed research will significantly contribute to the maximum use of RS data and products in many applications. Within the proposed budget and research scope at least ten graduate students and ten undergraduate students will be trained.

遥感数据已广泛用于环境监测和健康应用。长期以来，将多时相遥感数据集的光谱和模式变化与地表特征和生物物理变量的变化联系起来，一直是遥感技术的一项重要应用。虽然已经开发了各种变化检测方法，但是它们主要基于对从相同或相似传感器获取的多时相图像的图像分析。随着遥感技术的快速发展和大规模多尺度、多传感器对地观测数据集的日益可用，变化检测被迫纳入这些数据集，需要更有效的变化检测方法和更强大的工具来应对挑战。 本研究基于我们提出的基于目标的变化检测的创新概念，旨在通过以下方法提高变化检测的准确性：（i）评估不同光谱分解和亚像素方法在绘制目标土地覆盖及其在不同尺度上的变化时的性能;（ii）通过数据挖掘和从多光谱、多尺度、多时相遥感数据立方体为不同的目标在不同的景观;及（iii）开发一个评价框架，混合变化检测时，涉及多尺度和多传感器的时间图像。 该研究的长期目标是为更好地理解变化检测中的误差和不确定性以及从大量多传感器时间序列遥感图像和产品中检测模式和变化提供理论框架和强大的工具。创新的变化检测方法和评估框架，从拟议的研究将大大有助于最大限度地利用遥感数据和产品在许多应用。在拟议的预算和研究范围内，至少有十名研究生和十名本科生将接受培训。