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-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