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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Online Access: | http://dx.doi.org/10.1371/journal.pone.0290624 https://dx.plos.org/10.1371/journal.pone.0290624 |
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crplos:10.1371/journal.pone.0290624 2024-05-19T07:38:16+00:00 An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images P., Anilkumar P., Venugopal Sun, Xiaoyong 2023 http://dx.doi.org/10.1371/journal.pone.0290624 https://dx.plos.org/10.1371/journal.pone.0290624 en eng Public Library of Science (PLoS) http://creativecommons.org/licenses/by/4.0/ PLOS ONE volume 18, issue 10, page e0290624 ISSN 1932-6203 journal-article 2023 crplos https://doi.org/10.1371/journal.pone.0290624 2024-05-01T07:07:47Z 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. Article in Journal/Newspaper Beluga Beluga whale Beluga* PLOS PLOS ONE 18 10 e0290624 |
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English |
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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. |
author2 |
Sun, Xiaoyong |
format |
Article in Journal/Newspaper |
author |
P., Anilkumar P., Venugopal |
spellingShingle |
P., Anilkumar P., Venugopal An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images |
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 (PLoS) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1371/journal.pone.0290624 https://dx.plos.org/10.1371/journal.pone.0290624 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
PLOS ONE volume 18, issue 10, page e0290624 ISSN 1932-6203 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
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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1799477671096221696 |