Mr. Wei Xiang Profile

Wei Xiang

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 | 30 April 2024 Paper

Reshaping the feature distribution to aid in building extraction

Zhenshuai Chen, Hengjia Qin, Wei Xiang, Yunpeng Liu

Proceedings Volume 13157, 131570M (2024) https://doi.org/10.1117/12.3016494

KEYWORDS: Remote sensing, Feature extraction, Convolutional neural networks, Image segmentation, Semantics

Read Abstract +

In the field of remote sensing image interpretation, utilizing Convolutional Neural Networks (CNNs) for building extraction is a highly significant task. End-to-end building extraction methods typically consist of two key components: the encoder and the decoder. However, during building extraction facilitated by the encoder, down-sampling operations often lead to a loss of boundary features in the segmented objects. Many of these lost features correspond to the boundaries of buildings, and the reduction of smaller-scale boundaries features diminishes the network's attention to building boundary, resulting in blurred architectural delineation. In this paper, we propose the Reshape Feature Distribution (RFD-Net) network to alleviate the problem of boundary blurriness. We embed a reshaping feature distribution module within the network, which manipulates the data distribution of feature values by compressing the maximum values and elevating the minimum values. This module can effectively increase the magnitude of feature values at positions corresponding to building boundaries in the feature maps, subsequently enhancing the network's attention to building boundaries and alleviating the problem of boundary blurriness. We conducted experiments on the WHU dataset, demonstrating the effectiveness of our proposed approach.

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