Mr. Shunli Wang Profile

Shunli Wang

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 | 7 March 2024 Paper

Research on deep learning-based land cover extraction method using multi-source mixed samples

Yizhou Lan, Qingwu Hu, Shunli Wang, Jiayuan Li, Pengcheng Zhao, Mingyao Ai

Proceedings Volume 13088, 1308803 (2024) https://doi.org/10.1117/12.2689788

KEYWORDS: Land cover, Remote sensing, Deep learning, Feature extraction

Read Abstract +

The advancement of Earth observation technologies has enabled comprehensive Earth monitoring at unprecedented levels. Single remote sensing images often fall short in practical applications due to various limitations. In response, the field of remote sensing has evolved to encompass multiple temporal phases, wavelengths and sensors, resulting in a wealth of multi-source remote sensing data. However, most studies have primarily employed single remote sensing images as their data source, with limited research on deep learning for land cover classification using multi-source mixed samples. There is limited research on the influence of network band selection strategies for different land cover categories. Research into network band selection strategies further enhances the accuracy of land cover extraction from multi-source data. Consequently, this paper presents a methodology for deep learning with multi-source mixed samples, investigating strategies for selecting optimal spectral bands for different land cover categories and ultimately aiming to extract various typical land cover classes from multi-source remote sensing data within the same geographic region. We begin by creating a multi-source remote sensing mixed sample dataset and employ channel enhancement methods to enable the model to predict data with different spectral band combinations. Subsequently, we investigate the network's selection of optimal spectral bands for different land cover categories to enhance land cover extraction accuracy. The results indicate that our approach not only improves accuracy but also enhances the model's generalization capabilities.

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