Mr. Po Hu Profile

Po Hu

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

An iterative attention context fusion network for semantic segmentation of remote sensing image

Yanpu Chen, Haihui Wang, Shuiping Zhang, Po Hu

Proceedings Volume 13088, 130880B (2024) https://doi.org/10.1117/12.3011073

KEYWORDS: Image segmentation, Image fusion, Remote sensing, Semantics, Image quality, Target detection, Information fusion, Image resolution, Image classification, Homogenization

Read Abstract +

In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively.

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