RSDNet: Res-CBAM-enhanced integration of shallow and deep networks for ultra-high-resolution image segmentation

Kaifeng Chen; Xi Lin; Changshe Zhang; Yundong Wu

doi:10.1117/12.3029624

21 June 2024 RSDNet: Res-CBAM-enhanced integration of shallow and deep networks for ultra-high-resolution image segmentation

Kaifeng Chen, Xi Lin, Changshe Zhang, Yundong Wu

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671W (2024) https://doi.org/10.1117/12.3029624
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In the field of ultra-high-resolution image segmentation, challenges persist regarding high resource consumption. Prior research, exemplified by ISDNet, introduced an innovative dual-branch network and a relation-aware feature fusion module. This module was designed to integrate shallow and deep features in a novel manner, resulting in improved inference speed while maintaining accurate segmentation. Our study aims to address the limitations of the original research and introduce enhancements. In this paper, we propose RSDNet, a Dual-Branch Feature Enhancement Framework for Super-Resolution Segmentation. Specifically, in our approach, the feature fusion module of ISDNet was replaced with a feature enhancement module. This module employs a feature addition method to combine deep and shallow features. Experimental results on the Inria Aerial dataset demonstrate a substantial improvement in the trade-off between memory usage and accuracy compared to the original method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaifeng Chen, Xi Lin, Changshe Zhang, and Yundong Wu "RSDNet: Res-CBAM-enhanced integration of shallow and deep networks for ultra-high-resolution image segmentation", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671W (21 June 2024); https://doi.org/10.1117/12.3029624

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Feature fusion

Image fusion

Image resolution

Education and training

Image processing

Head

Show All Keywords

Keywords/Phrases

Search In:

Publication Years