An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images
Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentati...
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ftpubmed:oai:pubmedcentral.nih.gov:10615319 2023-12-03T10:20:15+01:00 An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images P., Anilkumar P., Venugopal 2023-10-30 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/ http://www.ncbi.nlm.nih.gov/pubmed/37903154 https://doi.org/10.1371/journal.pone.0290624 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/ http://www.ncbi.nlm.nih.gov/pubmed/37903154 http://dx.doi.org/10.1371/journal.pone.0290624 © 2023 P. P. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PLoS One Research Article Text 2023 ftpubmed https://doi.org/10.1371/journal.pone.0290624 2023-11-05T02:05:05Z Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models. Text Beluga Beluga whale Beluga* PubMed Central (PMC) PLOS ONE 18 10 e0290624 |
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Research Article P., Anilkumar P., Venugopal An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
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Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models. |
format |
Text |
author |
P., Anilkumar P., Venugopal |
author_facet |
P., Anilkumar P., Venugopal |
author_sort |
P., Anilkumar |
title |
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
title_short |
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
title_full |
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
title_fullStr |
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
title_full_unstemmed |
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
title_sort |
improved beluga whale optimizer—derived adaptive multi-channel deeplabv3+ for semantic segmentation of aerial images |
publisher |
Public Library of Science |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/ http://www.ncbi.nlm.nih.gov/pubmed/37903154 https://doi.org/10.1371/journal.pone.0290624 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
PLoS One |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/ http://www.ncbi.nlm.nih.gov/pubmed/37903154 http://dx.doi.org/10.1371/journal.pone.0290624 |
op_rights |
© 2023 P. P. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
op_doi |
https://doi.org/10.1371/journal.pone.0290624 |
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PLOS ONE |
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18 |
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10 |
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e0290624 |
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