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...
| Published in: | IEEE Access |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.1109/ACCESS.2023.3308495 https://doaj.org/article/0757fa4a56e74c168e80c20ee815e2bb |
| Summary: | 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. |
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