Lidar Crop Classification with Data Fusion and Machine Learning

被引：0
|
作者：
Prins, Adriaan Jacobus [1 ]
机构：
[1] Mallon Technol, Dublin, Ireland
来源：
GIM INTERNATIONAL-THE WORLDWIDE MAGAZINE FOR GEOMATICS | 2021年 / 35卷 / 03期
关键词：
D O I：
暂无
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Crop type maps are often generated using remotely sensed data acquired by sensors mounted on satellites, manned aircraft or unmanned aerial vehicles (UAVs or 'drones'), the most popular being multispectral sensors mounted on satellites. Aerial multispectral sensors are more frequently employed where imagery with very high spatial resolution is required. However, the use of Lidar data for crop type mapping is still uncommon. This article outlines research done on creating crop type maps using Lidar, Sentinel-2 and aerial data along with several machine learning classification algorithms for differentiating four crop types in an intensively cultivated area.
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页码：16 / 19
页数：4
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