Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction
Optical remote sensing is an important method of observing objects over large areas. Naturally, it is essential to extract the target from optical remote sensing images. Most existing methods, such as thresholding methods and texture analysis-based methods, have some limitations. Additionally, most...
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ftdoajarticles:oai:doaj.org/article:0757fa4a56e74c168e80c20ee815e2bb 2023-10-09T21:49:16+02:00 Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction Yizhen Xiong Difeng Wang Dongyang Fu Yan Wang 2023-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2023.3308495 https://doaj.org/article/0757fa4a56e74c168e80c20ee815e2bb EN eng IEEE https://ieeexplore.ieee.org/document/10229155/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2023.3308495 https://doaj.org/article/0757fa4a56e74c168e80c20ee815e2bb IEEE Access, Vol 11, Pp 92111-92119 (2023) Target object extraction discrete-time noise-suppression neural dynamics (DTNSND) model constrained energy minimization (CEM) scheme noise-suppression Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2023 ftdoajarticles https://doi.org/10.1109/ACCESS.2023.3308495 2023-09-10T00:36:56Z Optical remote sensing is an important method of observing objects over large areas. Naturally, it is essential to extract the target from optical remote sensing images. Most existing methods, such as thresholding methods and texture analysis-based methods, have some limitations. Additionally, most methods are generally not robust to noise, which tends to affect extraction results to some extent. Thus, how to extract the target object from optical remote sensing images conveniently and robustly is a challenge. To make up for the shortcomings of most methods, a constrained energy minimization (CEM) scheme is applied to extract the target object. Then, a discrete-time noise-suppression neural dynamics (DTNSND) model with an error-accumulation term is proposed to aid the CEM scheme for extracting the target object, which restrains the effects of noises in the extraction process. Theoretical analyses demonstrate that the DTNSND model suppresses noise in diverse noisy environments. Furthermore, numerical simulations are provided to illustrate that the maximal steady-state residual error generated by the DTNSND model is markedly lower than those of comparative algorithms. Finally, extraction experiments, using an optical remote sensing image of the Arctic sea ice as an experimental material, are executed in zero noise and random noise environments, respectively. Comparative results confirm that the DTNSND model is able to extract the remote sensing image stably and accurately in noisy environments, further demonstrating the feasibility of the DTNSND model in practice. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic IEEE Access 11 92111 92119 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Target object extraction discrete-time noise-suppression neural dynamics (DTNSND) model constrained energy minimization (CEM) scheme noise-suppression Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
spellingShingle |
Target object extraction discrete-time noise-suppression neural dynamics (DTNSND) model constrained energy minimization (CEM) scheme noise-suppression Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yizhen Xiong Difeng Wang Dongyang Fu Yan Wang Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
topic_facet |
Target object extraction discrete-time noise-suppression neural dynamics (DTNSND) model constrained energy minimization (CEM) scheme noise-suppression Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
Optical remote sensing is an important method of observing objects over large areas. Naturally, it is essential to extract the target from optical remote sensing images. Most existing methods, such as thresholding methods and texture analysis-based methods, have some limitations. Additionally, most methods are generally not robust to noise, which tends to affect extraction results to some extent. Thus, how to extract the target object from optical remote sensing images conveniently and robustly is a challenge. To make up for the shortcomings of most methods, a constrained energy minimization (CEM) scheme is applied to extract the target object. Then, a discrete-time noise-suppression neural dynamics (DTNSND) model with an error-accumulation term is proposed to aid the CEM scheme for extracting the target object, which restrains the effects of noises in the extraction process. Theoretical analyses demonstrate that the DTNSND model suppresses noise in diverse noisy environments. Furthermore, numerical simulations are provided to illustrate that the maximal steady-state residual error generated by the DTNSND model is markedly lower than those of comparative algorithms. Finally, extraction experiments, using an optical remote sensing image of the Arctic sea ice as an experimental material, are executed in zero noise and random noise environments, respectively. Comparative results confirm that the DTNSND model is able to extract the remote sensing image stably and accurately in noisy environments, further demonstrating the feasibility of the DTNSND model in practice. |
format |
Article in Journal/Newspaper |
author |
Yizhen Xiong Difeng Wang Dongyang Fu Yan Wang |
author_facet |
Yizhen Xiong Difeng Wang Dongyang Fu Yan Wang |
author_sort |
Yizhen Xiong |
title |
Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
title_short |
Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
title_full |
Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
title_fullStr |
Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
title_full_unstemmed |
Discrete-Time Noise-Suppression Neural Dynamics for Optical Remote Sensing Image Extraction |
title_sort |
discrete-time noise-suppression neural dynamics for optical remote sensing image extraction |
publisher |
IEEE |
publishDate |
2023 |
url |
https://doi.org/10.1109/ACCESS.2023.3308495 https://doaj.org/article/0757fa4a56e74c168e80c20ee815e2bb |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
IEEE Access, Vol 11, Pp 92111-92119 (2023) |
op_relation |
https://ieeexplore.ieee.org/document/10229155/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2023.3308495 https://doaj.org/article/0757fa4a56e74c168e80c20ee815e2bb |
op_doi |
https://doi.org/10.1109/ACCESS.2023.3308495 |
container_title |
IEEE Access |
container_volume |
11 |
container_start_page |
92111 |
op_container_end_page |
92119 |
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1779312279529979904 |