Deep Learning-Based Land Cover Classification Using Airborne Lidar and Aerial Imagery
碩士 === 國立交通大學 === 土木工程系所 === 107 === Land cover classification has always been a critical issue. People tried to learn information from the distribution of land cover; as it may affect climate, environment or structure of society. Land cover information can be extracted from spectral feature, geomet...
Main Authors: | Yang, Chu-Chun, 楊筑鈞 |
---|---|
Other Authors: | Teo, Tee-Ann |
Format: | Others |
Language: | en_US |
Published: |
2019
|
Online Access: | http://ndltd.ncl.edu.tw/handle/fv26ta |
Similar Items
-
Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods
by: You Mo, et al.
Published: (2019-04-01) -
A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery
by: Jin, Jiao
Published: (2012) -
A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery
by: Jin, Jiao
Published: (2012) -
EVALUATING THE POTENTIAL OF MULTISPECTRAL AIRBORNE LIDAR FOR TOPOGRAPHIC MAPPING AND LAND COVER CLASSIFICATION
by: V. Wichmann, et al.
Published: (2015-08-01) -
Echo Detection and Land Cover Classification of Airborne Waveform LiDAR Data
by: Cheng-KaiWang, et al.
Published: (2015)