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土地覆盖变化检测的模型时间序列分割方法
A Model-Free Time Series Segmentation Approach for Land Cover Change Detection |
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| 课程网址: |
http://videolectures.net/cidu2011_garg_detection/
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| 主讲教师: |
Ashish Garg
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| 开课单位: |
明尼苏达大学
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| 开课时间: |
2012-06-27
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| 课程语种: |
英语
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| 中文简介: |
从卫星上的遥感器进行的与生态系统有关的观测 了解全球土地覆被变化的地点和程度的巨大可能性。本文重点研究了土地覆被变化检测背景下的时间序列分割技术。在统计领域提出的事件检测框架的启发下, 提出了一种基于模型的时间序列分割算法。提出了一种新的无模型变化检测算法, 该算法具有计算简单、高效、非参数的特点, 并考虑到遥感数据中存在的固有变异性。这种方法的一个关键优点是, 它可以在全球范围内应用于各种植被, 而不必为特定植被类型确定正确的模型。我们评估了合成数据集和 modis 经济脆弱性数据集上所建议技术的变化检测能力。我们说明了 解释 evi 数据集中的自然变化的各种算法。
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| 课程简介: |
Ecosystem-related observations from remote sensors on satellites o er signi ficant possibility for understanding the location and extent of global land cover change. In this paper, we focus on time series segmentation techniques in the context of land cover change detection. We propose a model-based time series segmentation algorithm inspired by an event detection framework proposed in the field of statistics. We also present a novel model-free change detection algorithm for detecting land cover change that is computationally simple, efficient, non-parametric and takes into account the inherent variability present in the remote sensing data. A key advantage of this method is that it can be applied globally for a variety of vegetation without having to identify the right model for specifi c vegetation types. We evaluate the change detection capacity of the proposed techniques on both synthetic and MODIS EVI data sets. We illustrate the importance and relative ability of di fferent algorithms to account for the natural variation in the EVI data set.
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| 关 键 词: |
计算机科学; 数据挖掘; 时间序列分析
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| 课程来源: |
视频讲座网公开课
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| 最后编审: |
2020-06-03：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
41
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