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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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Detalles Bibliográficos
Autores principales: P., Anilkumar, P., Venugopal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/
https://www.ncbi.nlm.nih.gov/pubmed/37903154
http://dx.doi.org/10.1371/journal.pone.0290624
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author P., Anilkumar
P., Venugopal
author_facet P., Anilkumar
P., Venugopal
author_sort P., Anilkumar
collection PubMed
description 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.
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spelling pubmed-106153192023-10-31 An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images P., Anilkumar P., Venugopal PLoS One Research Article 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. Public Library of Science 2023-10-30 /pmc/articles/PMC10615319/ /pubmed/37903154 http://dx.doi.org/10.1371/journal.pone.0290624 Text en © 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.
spellingShingle Research Article
P., Anilkumar
P., Venugopal
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615319/
https://www.ncbi.nlm.nih.gov/pubmed/37903154
http://dx.doi.org/10.1371/journal.pone.0290624
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