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

05137591.pdf
1-s2.0-S0031320307001677-main.pdf

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．