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