Comparison of Spectral Indices and Principal Component Analysis for Differentiating Lodged Rice Crop from Normal Ones

  • Zhanyu Liu
  • Cunjun Li
  • Yitao Wang
  • Wenjiang Huang
  • Xiaodong Ding
  • Bin Zhou
  • Hongfeng Wu
  • Dacheng Wang
  • Jingjing Shi
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 369)


Hyperspectral reflectance of normal and lodged rice caused by rice brown planthopper and rice panicle blast was measured at the canopy level. Over one decade broad- and narrow-band vegetation indices (VIs) were calculated to simulate Landsat ETM+ with in situ hyperspectral reflectance. Principal component analysis (PCA) was utilized to obtain the front two principal components (PCs). Probabilistic neural network (PNN) was employed to classify the lodged and normal rice with VIs and PCs as the input vectors. PCs had 100% of overall accuracy and 1 of Kappa coefficient for the training dataset. While PCs had the greatest average overall accuracy (97.8%) and Kappa coefficient (0.955) for the two testing datasets than VIs consisting of broad- and narrow-bands. The results indicated that hyperspectral remote sensing with PCA and artificial neural networks could potentially be applied to discriminate lodged crops from normal ones at regional and large spatial scales.


Hyperspectral remote sensing Lodged rice Principal component analysis (PCA) Vegetation indices (VIs) Artificial neural networks (ANN) 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Zhanyu Liu
    • 1
    • 2
  • Cunjun Li
    • 3
  • Yitao Wang
    • 4
  • Wenjiang Huang
    • 3
  • Xiaodong Ding
    • 1
  • Bin Zhou
    • 1
  • Hongfeng Wu
    • 4
  • Dacheng Wang
    • 3
  • Jingjing Shi
    • 5
  1. 1.Institute of Remote Sensing and Earth SciencesHangzhou Normal UniversityHangzhouChina
  2. 2.Key Laboratory of Urban Wetland and Region ChangeHangzhouChina
  3. 3.Beijing Research Center for Information Technology in AgricultureBeijingChina
  4. 4.Institute of Scientific and Technological InformaticsHeilongjiang Academy of Land Reclamation SciencesHarbinChina
  5. 5.Institute of Agricultural Remote Sensing & Information TechnologyZhejiang UniversityHangzhouChina

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