Description: Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages
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