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Supervised spatial classification of multispectral LiDAR data in urban areas.

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  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.

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This study improved urban land cover classification using multispectral LiDAR data. Combining LiDAR intensity, pseudoNDVI, nDSM, and hierarchical morphological profiles achieved 93.28% overall accuracy.

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Urban Ecology

Background:

  • Multispectral LiDAR (light detection and ranging) data show promise for land cover classification.
  • High uncertainties persist in urban areas due to mixed and confounded objects.

Purpose of the Study:

  • To enhance land cover classification accuracy in urban environments.
  • To investigate the effectiveness of combining statistical methods and LiDAR metrics from multispectral LiDAR data.

Main Methods:

  • Utilized multispectral Optech Titan LiDAR data (1550 nm, 1064 nm, 532 nm).
  • Derived features including LiDAR intensity, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and hierarchical morphological profiles (HMP).
  • Applied a support vector machine (SVM) classifier with a radial basis function (RBF) kernel, optimizing parameters via grid search.

Main Results:

  • The optimal feature combination included intensity, PseudoNDVI, nDSM, and HMP.
  • Achieved an overall land cover classification accuracy of 93.28%.

Conclusions:

  • The integration of advanced statistical methods and specific LiDAR metrics significantly improves urban land cover classification.
  • Multispectral LiDAR data, when processed with sophisticated techniques, offers a robust solution for detailed urban environmental mapping.