مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

569
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

0
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

A NOVEL METHOD FOR CLASSIFICATION OF MULTI RETURNS LIDAR DATA USING GEOMETRICAL-CONTEXTUAL INFORMATION AND PROTOTYPE SPACE

Pages

  15-23

Keywords

AIRBORNE LASER SCANNERS (ALS)Q1

Abstract

 High accuracy and huge density of 3D points cloud acquired by airborne Lidar makes them as a good and suitable tool in order to analyze of terrain surface. In this procedure, points cloud CLUSTERING is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper a novel method is proposed for supervised classification of LiDAR points cloud based on CONTEXTUAL ANALYSIS on LiDAR points. The proposed method consists of three main steps. In the first step, a set of contextual features are produced for each points in LiDAR data. In second step, optimum FEATURE SELECTION is done in the modified PROTOTYPE SPACE using a new strategy. The last step is conducted to a simple k-means CLUSTERING on the FEATURE SPACE spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluation of the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients was 93.15% and 0.89 respectively.

Cites

  • No record.
  • References

  • No record.
  • Cite

    APA: Copy

    SAFDARINEZHAD, ALIREZA, MOKHTARZADEH, MAHDI, & VALADANZOUJ, MOHAMMAD JAVAD. (2016). A NOVEL METHOD FOR CLASSIFICATION OF MULTI RETURNS LIDAR DATA USING GEOMETRICAL-CONTEXTUAL INFORMATION AND PROTOTYPE SPACE. GEOGRAPHICAL DATA, 25(98), 15-23. SID. https://sid.ir/paper/253214/en

    Vancouver: Copy

    SAFDARINEZHAD ALIREZA, MOKHTARZADEH MAHDI, VALADANZOUJ MOHAMMAD JAVAD. A NOVEL METHOD FOR CLASSIFICATION OF MULTI RETURNS LIDAR DATA USING GEOMETRICAL-CONTEXTUAL INFORMATION AND PROTOTYPE SPACE. GEOGRAPHICAL DATA[Internet]. 2016;25(98):15-23. Available from: https://sid.ir/paper/253214/en

    IEEE: Copy

    ALIREZA SAFDARINEZHAD, MAHDI MOKHTARZADEH, and MOHAMMAD JAVAD VALADANZOUJ, “A NOVEL METHOD FOR CLASSIFICATION OF MULTI RETURNS LIDAR DATA USING GEOMETRICAL-CONTEXTUAL INFORMATION AND PROTOTYPE SPACE,” GEOGRAPHICAL DATA, vol. 25, no. 98, pp. 15–23, 2016, [Online]. Available: https://sid.ir/paper/253214/en

    Related Journal Papers

    Related Seminar Papers

  • No record.
  • Related Plans

  • No record.
  • Recommended Workshops






    Move to top
    telegram sharing button
    whatsapp sharing button
    linkedin sharing button
    twitter sharing button
    email sharing button
    email sharing button
    email sharing button
    sharethis sharing button