Prof. Yinglei Cheng Profile

Prof. Yinglei Cheng

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 16 February 2022 Paper

HA-GCN: an ALS point cloud classification method based on height-aware graph convolution network

Pei Wen, Yinglei Cheng, Peng Wang, Mingjun Zhao, Bixiu Zhang

Proceedings Volume 12083, 120831C (2022) https://doi.org/10.1117/12.2623390

KEYWORDS: Clouds, Convolution, Feature extraction, LIDAR, Classification systems, Data modeling, Buildings, Vegetation, Remote sensing, 3D acquisition

Read Abstract +

In this paper, we propose a Height-Aware Graph Convolution Network (HA-GCN) to solve the challenging problem of Airborne laser scanning (ALS) point cloud classification. For samples with uneven distribution and large differences in scale, classification using local features is unstable and easily affected by noise. Therefore, we use a multi-layer stacked Edge Convolution (EdgeConv) operators to extract local and global information at the same time. In addition, in view of the characteristics of the height distribution of airborne LiDAR point cloud, we introduce height attention weights as a supplement to feature extraction. First, the original point cloud is divided into sub-blocks and sampled to a fixed number of points. Then, the EdgeConv operator is used to extract local-global features. At the same time, the Height-Aware (HA) module is used to generate attention weights. Finally, the height attention weights are applied to the feature extraction network and the classification is completed after post-processing. The experimental results on the Vaihingen dataset show that the proposed method achieves the effect of the state-of-the-art methods in overall accuracy, as well as impressive results in single-category classification accuracy.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years