Application of LiDAR’s multiple attributes for wetland classification

Qiong Ding; Shengyue Ji; Wu Chen

doi:10.1117/12.2234678

2 March 2016 Application of LiDAR’s multiple attributes for wetland classification

Qiong Ding, Shengyue Ji, Wu Chen

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 990110 (2016) https://doi.org/10.1117/12.2234678
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

Abstract

Wetlands have received intensive interdisciplinary attention as a unique ecosystem and valuable resources. As a new technology, the airborne LiDAR system has been applied in wetland research these years. However, most of the studies used only one or two LiDAR observations to extract either terrain or vegetation in wetlands. This research aims at integrating LiDAR’s multiple attributes (DSM, DTM, off-ground features, Slop map, multiple pulse returns, and normalized intensity) to improve mapping and classification of wetlands based on a multi-level object-oriented classification method. By using this method, we are able to classify the Yellow River Delta wetland into eight classes with overall classification accuracy of 92.5%

Citation Download Citation

Qiong Ding, Shengyue Ji, and Wu Chen "Application of LiDAR’s multiple attributes for wetland classification", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990110 (2 March 2016); https://doi.org/10.1117/12.2234678

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available