Dr. Xuqing Li Profile

Dr. Xuqing Li

at North China Institute of Aerospace Engineering

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 May 2024 Paper

Semantic information extraction and automatic interpretation of winter wheat based on a deformable full convolutional neural network

Xuqing Li, Yubo Wang, Huitao Gu, Dongxue Wu, Qinxue Zhang

Proceedings Volume 13166, 131660U (2024) https://doi.org/10.1117/12.3029284

KEYWORDS: Education and training, Convolution, Deformation, Data modeling, Remote sensing, Machine learning, Semantics, Convolutional neural networks, Statistical modeling, Image processing

Read Abstract +

Timely and accurate agricultural monitoring can provide effective guidance for crop management but the process can be challenging. In this study, a GF-2 remote sensing image was used as the research object to extract the winter wheat based on an improved deforming full convolutional neural network (DFCN) model. The deformable convolution model was developed to try to better extract geometric features and object-level semantic information and improve the ability to describe the different spatial distribution characteristics of winter wheat. Convolution deformation was obtained by adding a trainable two-dimensional migration to each convolution layer in the network. The DFCN network model was compared to traditional neural networks such as FCN and U-Net. The precision ratio of DFCN was improved to 98.1%,and the time required was reduced to 0.63 seconds. In contrast, the accuracies of winter wheat automatic interpretation obtained by FCN and U-Net were 89.3% and 93.9%, respectively. The experimental results show that using the DFCN model to interpret winter wheat automatically can achieve the highest accuracy, better capturing its morphology and spatial distribution, and with good generalization performance. Through the training of the input data set and parameter tuning, a high-precision intelligent and DFCN model with high practicability and reliability was constructed, and a high-precision automatic remote sensing identification system for typical crops in the north of the Huang-Huai-Hai Plain was realized.

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