Multiscale deep fully convolutional network for sea-land segmentation of surveillance images

Xiaoyu Wu; Gong Chen; Xiangqian Ding; KeZhen Xie; Guanqun Wei; Wanlin Lu

doi:10.1117/12.2579189

10 November 2020 Multiscale deep fully convolutional network for sea-land segmentation of surveillance images

Xiaoyu Wu, Gong Chen, Xiangqian Ding, KeZhen Xie, Guanqun Wei, Wanlin Lu

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158406 (2020) https://doi.org/10.1117/12.2579189
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Sea-Land segmentation based on surveillance images is an important research content for real-time coast monitoring. However, the complex weather and environmental makes the segmentation of sea-land is a difficult task. Although previous deep learning methods based on convolutional neural networks have achieved excellent results in semantic segmentation, and there has been some work using deep convolutional neural networks for Sea-Land segmentation but we hope that the image segmentation model can achieve more accurate results in sea and land segmentation. In our method, we propose a novel sea-land segmentation framework called Multi Sea-Land U-net (MSLUnet), the framework base on a multi-scale. The proposed MSLUnet is mainly composed of a multi-scale layer and U-Net convolutional network. The multi-scale input layer constructs an image pyramid to accept multiple levels of image data in the network model. U-shaped convolutional networks are used as the back-bone network structure to learn rich hierarchical representations. Experimental results show that compared with other architectures, MSLUnet has achieved good performance.

Citation Download Citation

Xiaoyu Wu, Gong Chen, Xiangqian Ding, KeZhen Xie, Guanqun Wei, and Wanlin Lu "Multiscale deep fully convolutional network for sea-land segmentation of surveillance images", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158406 (10 November 2020); https://doi.org/10.1117/12.2579189

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

Surveillance

Data modeling

Remote sensing

Convolutional neural networks

Feature extraction

Computer programming

Show All Keywords

Keywords/Phrases

Search In:

Publication Years