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An adaptive multichannel DeepLabv3 + for semantic segmentation of aerial images using improved Beluga Whale Optimization Algorithm

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Abstract

Semantic segmentation of aerial images plays a pivotal role in extracting detailed information about land cover, infrastructure, and natural features. Traditional single-channel segmentation models struggle to harness the rich information present in multi-channel aerial images, such as multi-spectral or hyperspectral data. While the DeepLabV3 + architecture has shown remarkable success in semantic segmentation tasks by exploiting multi-scale context and atrous convolutions, its performance on aerial images remains suboptimal due to the unique challenges of this domain. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. A multi-objective derived Adaptive Multichannel deeplabv3 + (AMC-Deeplabv3 +) with a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO) algorithm is proposed in this paper. Here, the hyperparameters of Multichannel deeplabv3 + are optimized by the IBWO algorithm and this model intends to set new benchmarks in the accuracy and contextual understanding of aerial image segmentation by integrating multi-channel data processing techniques and preserving spatial context. 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 s. The evolutional outcomes of the proposed model show significantly better than the state-of-the-art techniques.

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Data availability statement

The data underlying this article are available in Semantic segmentation of aerial imagery “https://www.kaggle.com/datasets/humansintheloop/semantic-segmentation-of-aerialimagery?select=Semantic+segmentation+dataset”.

Aerial image segmentation dataset http://jiangyeyuan.com/ASD/Aerial%20Image%20Segmentation%20Dataset.html.

Notes

  1. https://www.kaggle.com/humansintheloop/semantic-segmentation-of-aerialimagery?select=Semantic+segmentation+datasetimagery?select=Semantic+segmentation+dataset

  2. http://jiangyeyuan.com/ASD/Aerial%20Image%20Segmentation%20Dataset.html

Abbreviations

CNN:

Convolutional Neural Networks

DCNN:

Deep Convolutional Neural Network

IBWO:

Improved Beluga whale optimization

FCN:

Fully Convolutional Neural Network

IRRG:

Infrared, Red, and Green

DRN:

Deep Residual Networks

DFCN:

Deep Fully Convolutional Network

CAM-DFCN:

Channel Attention Mechanism-Deep Fully Convolutional Neural Network

ASPP:

Atrous Spatial Pyramid Pooling

UAV:

Unmanned Aerial Vehicle

OSM:

Open Street Map

GAN:

Generative Adversarial Network

DSM:

Digital Surface Models

DSMF:

Digital Surface Models Fusion

FoV:

Field-of-View

VHR:

Very High Resolution

BWO:

Beluga Whale Optimization

GSO:

Glow-worm Swarm Optimization

BOA:

Butterfly Optimization Algorithm

COA:

Coyote Optimization Algorithm

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Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    P. Anilkumar & P. Venugopal

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  1. P. Anilkumar
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  2. P. Venugopal
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Contributions

Conceptualization, V.P; methodology, V.P; software, A.P.; validation, A.P; investigation, A.P; resources, V.P and A.P; writing—original draft preparation, A.P; writing—review and editing, V.P; visualization, V.P; supervision, V.P;

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Correspondence to P. Venugopal.

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Anilkumar, P., Venugopal, P. An adaptive multichannel DeepLabv3 + for semantic segmentation of aerial images using improved Beluga Whale Optimization Algorithm. Multimed Tools Appl 83, 46439–46478 (2024). https://doi.org/10.1007/s11042-023-17247-z

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