• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Liu, Tianyu (Liu, Tianyu.) | Liu, Pengyu (Liu, Pengyu.) | Jia, Xiaowei (Jia, Xiaowei.) | Chen, Shanji (Chen, Shanji.) | Ma, Ying (Ma, Ying.) | Gao, Qian (Gao, Qian.)

收录:

EI Scopus

摘要:

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land segmentation task. We have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy, the number of parameters and the time cost for prediction. The test results on remote sensing data sets of Guam, Okinawa, Taiwan, San Diego, and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters, reduces prediction time cost and improves performance over other mainstream segmentation models. We also show that the position encoding can further improve the accuracy of model segmentation. © 2023 CRL Publishing. All rights reserved.

关键词:

Image segmentation Signal encoding Natural language processing systems Image enhancement Remote sensing Convolution Deep learning Encoding (symbols)

作者机构:

  • [ 1 ] [Liu, Tianyu]The Information Department, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Liu, Tianyu]Advanced Information Network Beijing Laboratory, Beijing; 100124, China
  • [ 3 ] [Liu, Tianyu]Computational Intelligence and Intelligent Systems Beijing key Laboratory, Beijing; 100124, China
  • [ 4 ] [Liu, Pengyu]The Information Department, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Liu, Pengyu]School of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining; 810000, China
  • [ 6 ] [Liu, Pengyu]Advanced Information Network Beijing Laboratory, Beijing; 100124, China
  • [ 7 ] [Liu, Pengyu]Computational Intelligence and Intelligent Systems Beijing key Laboratory, Beijing; 100124, China
  • [ 8 ] [Jia, Xiaowei]Department of Computer Science, University of Pittsburgh, Pittsburgh; 15260, United States
  • [ 9 ] [Chen, Shanji]School of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining; 810000, China
  • [ 10 ] [Ma, Ying]School of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining; 810000, China
  • [ 11 ] [Gao, Qian]The Information Department, Beijing University of Technology, Beijing; 100124, China
  • [ 12 ] [Gao, Qian]Advanced Information Network Beijing Laboratory, Beijing; 100124, China
  • [ 13 ] [Gao, Qian]Computational Intelligence and Intelligent Systems Beijing key Laboratory, Beijing; 100124, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

Computer Systems Science and Engineering

ISSN: 0267-6192

年份: 2023

期: 2

卷: 45

页码: 1033-1045

2 . 2 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 4

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:322/4897286
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司