Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

DSSM : a deep neural network with spectrum separable module for multi-spectral remote sensing image segmentation

Tools
- Tools
+ Tools

Zhu, Hongming, Tan, Rui, Han, Letong, Fan, Hongfei, Wang, Zeju, Du, Bowen, Liu, Sicong and Liu, Qin (2022) DSSM : a deep neural network with spectrum separable module for multi-spectral remote sensing image segmentation. Remote Sensing, 14 (4). e818. doi:10.3390/rs14040818 ISSN 2072-4292.

[img]
Preview
PDF
WRAP-DSSM-deep-neural-network-spectrum-separable-module-multi-spectral-remote-sensing-image-segmentation-Du-2022.pdf - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (10Mb) | Preview
Official URL: https://doi.org/10.3390/rs14040818

Request Changes to record.

Abstract

Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimental results obtained on Zurich and Potsdam datasets prove that the spectrum-separable module (SSM) extracts more informative spectral features, and the proposed approach improves the segmentation accuracy without increasing GPU consumption.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Neural networks (Computer science), Deep learning (Machine learning), Image segmentation, Multispectral imaging
Journal or Publication Title: Remote Sensing
Publisher: MDPI AG
ISSN: 2072-4292
Official Date: 9 February 2022
Dates:
DateEvent
9 February 2022Published
31 January 2022Accepted
Volume: 14
Number: 4
Article Number: e818
DOI: 10.3390/rs14040818
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 7 March 2022
Date of first compliant Open Access: 9 March 2022
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
2018YFB0505001National Key R&D Program of ChinaUNSPECIFIED
61702374[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
21ZR1465100Natural Science Foundation of Shanghaihttp://dx.doi.org/10.13039/100007219

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us