A Machine-Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data (vol 11, pg 3453, 2019)

被引：0

作者：

Pourshamsi, Maryam ^{[1
]}

Garcia, Mariano ^{[2
]}

Lavalle, Marco ^{[3
]}

Balzter, Heiko ^{[4
,5
]}

机构：

[1] Univ Leicester, Ctr Landscape & Climate Res, Sch Geog Geol & Environm, Leicester LE1 7RH, Leics, England

[2] Univ Alcala, Dept Geol Geog & Environm, Madrid 28801, Spain

[3] CALTECH, Jet Prop Lab, Radar Sci & Engn Grp, Pasadena, CA 91125 USA

[4] Univ Leicester, Ctr Landscape & Climate Res, Sch Geog Geol & Environm, Leicester LE1 7RH, Leics, England

[5] Univ Leicester, Natl Ctr Earth Observat, Leicester LE1 7RH, Leics, England

来源：

关键词：

Machine learning; Synthetic aperture radar; Lidar radar;

D O I：

10.1109/JSTARS.2020.2968779

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Presents corrections to the above mentioned paper.

引用

页码：566 / 566

页数：1