TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

被引：2
|
作者：
Li, N. [1 ,2 ]
Liu, C. [1 ]
Pfeifer, N. [2 ]
Yin, J. F. [3 ]
Liao, Z. Y. [3 ]
Zhou, Y. [1 ]
机构：
[1] Tongji Univ, Coll Survey & Geoinformat, Shanghai 200092, Peoples R China
[2] Tech Univ Wien, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria
[3] Tongji Univ, Dept Math, Shanghai 200092, Peoples R China
来源：
XXIII ISPRS CONGRESS, COMMISSION III | 2016年 / 41卷 / B3期
基金：
中国国家自然科学基金;
关键词：
Feature Selection; Tensor Processing; KNN classification; FEATURES;
D O I：
10.5194/isprsarchives-XLI-B3-283-2016
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the "raw" data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.
引用
收藏
页码：283 / 287
页数：5
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