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首页> 外文会议>2011 IEEE International Geoscience Remote Sensing Symposium >Improving change vector analysis in Multi-temporal space to detect land cover changes by using cross-correlogram spectral matching algorithm
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Improving change vector analysis in Multi-temporal space to detect land cover changes by using cross-correlogram spectral matching algorithm

机译：利用跨相关图谱匹配算法改进多时空变化矢量分析，检测土地覆被变化

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Traditional Change Vector Analysis in Multi-temporal space (TCVAM) can effectively extract land cover change information based on VI time series, and it has been one of the main methods to detect land cover change at large scale. However, the TCVAM may exaggerate the change information and mix the land cover conversion and land cover modification because of the oversensitivity to the changes of VI values. The paper proposes an Improved Change Vector Analysis in Multi-temporal space (ICVAM) based on cross-correlogram spectral matching algorithm and applies it in the Beijing-Tianjin-Tangshan urban agglomeration district, China, using MODIS_EVI time series data to test the performance of the ICVAM. The results demonstrated the improvement of the ICVAM compared to the TCVAM: overall accuracy increased by 10.80% and the kappa coefficient increased by 0.13. The ICVAM has great potential to be widely used for land cover change detection based on VI time series at large scale.
机译：传统的多时空变化矢量分析（TCVAM）可以有效地提取基于VI时间序列的土地覆盖变化信息，已成为大规模检测土地覆盖变化的主要方法之一。但是，由于对VI值的更改过于敏感，TCVAM可能会夸大更改信息并混合土地覆被转换和土地覆被修改。提出了一种基于互相关图谱匹配算法的改进的多时空变化矢量分析方法（ICVAM），并将其应用于中国的京津唐城市群地区，利用MODIS_EVI时间序列数据来检验其性能。 ICVAM。结果表明，与TCVAM相比，ICVAM有所改进：整体精度提高了10.80％，卡伯系数提高了0.13。 ICVAM具有广阔的潜力，可广泛用于大规模基于VI时间序列的土地覆被变化检测。

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