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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images
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Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images

机译：扩展成对的可分离性指数以使用时间序列MODIS图像进行多作物鉴定

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摘要

The pairwise separability index (SI) has been demonstrated as an effective indicator for capturing crucial phenological differences between two plant species. However, its application to crop types, which have more obvious phenological characteristics than natural vegetation, has received less attention, and extending the pairwise SI to multiple crops for feature selection still remains a challenge. This paper presented two SI extension approaches (SIave and SImin) to select the optimal spectro-temporal features for multiple crops, and investigated their classification performance using Heilongjiang Province, China, as a study area. Feature interpretability and classification accuracy of different crops were evaluated for the two approaches. The results showed that the SIave approach generally has relatively high feature interpretability due to its better description of crucial phenological characteristics of different crops. Those crops with high separability are insensitive to the extension approach and have similar classification accuracy for the two approaches, whereas those crops with poor separability show good performance with the SImin method. Due to the higher temporal autocorrelation, the optimal features for crop classification that are selected by the SIave approach exhibit greater information redundancy across the time domain than those that are selected by the SImin approach, which largely explains the relatively low classification accuracy achieved using the SIave approach. These comparison results between SImin and SIave approaches also indicate that time-series images with high temporal resolution do not necessarily produce high classification accuracy, regardless of their ability to describe the seasonal characteristics of crops.
机译：成对的可分离性指数（SI）已被证明是捕获两种植物物种之间重要的物候差异的有效指标。然而，将其应用于比自然植被具有更明显的物候特征的农作物类型，受到的关注较少，并且将成对SI扩展到多种作物以进行特征选择仍然是一个挑战。本文介绍了两种SI扩展方法（SIave和SImin），以选择多种作物的最佳光谱时间特征，并以中国黑龙江省为研究对象，研究了它们的分类性能。两种方法评估了不同作物的特征可解释性和分类准确性。结果表明，SIave方法由于可以更好地描述不同农作物的关键物候特征，因此通常具有较高的特征可解释性。那些具有高可分离性的农作物对扩展方法不敏感，并且两种方法的分类精度都差不多，而那些具有高分离性的农作物在使用SImin方法时表现良好。由于较高的时间自相关性，SIave方法选择的最佳农作物分类特征在时域上显示出比SImin方法选择的最佳农作物信息冗余度，这很大程度上解释了SIave实现的分类精度相对较低方法。 SImin和SIave方法之间的这些比较结果还表明，具有高时间分辨率的时间序列图像不一定能产生高分类精度，无论它们描述作物的季节性特征的能力如何。

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  • 来源
    《IEEE Transactions on Geoscience and Remote Sensing》 |2016年第11期|6349-6361|共13页
  • 作者

    Qiong Hu; Wenbin Wu; Qian Song; Qiangyi Yu; Miao Lu; Peng Yang; Huajun Tang; Yuqiao Long;

  • 作者单位

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Remote Sensing Technology Center, Chinese Academy of Agricultural Sciences, Heilongjiang Academy of Agriculture Sciences, Beijing, Harbin, ChinaChina;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

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  • 正文语种 eng
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  • 关键词

    Agriculture; Silicon; MODIS; Indexes; Vegetation mapping; Remote sensing; Spatial resolution;

    机译：农业;硅;MODIS;指标;植被图;遥感;空间分辨率;

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