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...
| Published in: | PLOS ONE |
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| Main Authors: | , |
| Format: | Text |
| Language: | English |
| Published: |
Public Library of Science
2023
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| Subjects: | |
| Online Access: | 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 |
| Summary: | 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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