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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Mining Multitemporal In Situ Heterogeneous Monitoring Information for the Assurance of Recorded Land Cover Changes
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Mining Multitemporal In Situ Heterogeneous Monitoring Information for the Assurance of Recorded Land Cover Changes

机译：挖掘多时间原位异构监测信息以确保记录的土地覆被变化

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摘要

We present a data mining methodology to filter and validate land cover change detections obtained from multitemporal in situ surveys. As in situ data we use the measurements from the European land use and coverage area frame survey (LUCAS), which provides images with standardized metadata about land cover and land use within the whole territory of the European Union. Multitemporal LUCAS surveys present an anomaly in the amount of land cover changes that disagree with the estimated by experts. Therefore, our methodology analyses the available data in order to explain the existing irregularities in them. The initial step of our methodology is based on database query refinements. The data mining methodology continues with an image analysis process. This analysis calculates similarity measures of the multitemporal images that are used to identify the potential misclassifications. The final step involves a geographic information system based on web technologies. By defining different color codes assigned by the similarity measures, the system represents the examined points on a digital Earth globe. There, a user can easily discriminate potentially misclassified points for subsequent detailed analysis or corrections. The final output of the methodology shows remarkable results for detecting misclassified land cover changes.
机译：我们提出了一种数据挖掘方法，以过滤和验证从多时相原位调查中获得的土地覆盖变化检测结果。作为原位数据，我们使用了欧洲土地利用和覆盖区域框架调查（LUCAS）的测量结果，该测量结果为图像提供了有关欧盟整个领土内土地覆盖和土地利用的标准化元数据。 LUCAS多时相调查显示，土地覆盖变化量异常，与专家的估计不一致。因此，我们的方法分析了可用数据，以解释其中存在的不合规定之处。我们方法的第一步是基于数据库查询优化。数据挖掘方法继续进行图像分析过程。该分析计算了用于识别潜在错误分类的多时间图像的相似性度量。最后一步涉及基于Web技术的地理信息系统。通过定义相似性度量分配的不同颜色代码，系统可以表示数字地球仪上的检查点。在那里，用户可以轻松地区分潜在错误分类的点，以进行后续的详细分析或更正。该方法的最终输出显示出在发现分类错误的土地覆被变化方面的显着结果。

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