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<p>Aerial imagesemantic segmentation is crucial for various operations such as military observation, land classification, and disaster impact assessments involving unmanned aerial vehicles. Although existing system is&#xa0;unsuited for aerial applications,&#xa0;these algorithms are mostly trained on human-centric datasets like “Cityscapes and Cam Vid”. High-resolution aerial image semantic segmentation is a basic and difficult task with several applications. Even though numerous Convolution Neural Network (CNN) segmentation techniques have shown impressive results, it is still challenging to discriminate semantic parts among regions with comparable spectral properties employing only high-resolution data. Additionally, the typical data-independent up-sampling techniques could produce poor outcomes. Thus, a novel semantic segmentation technique is introduced to resolve the complication presented in the classical segmentation framework in aerial images by utilizing deep learning techniques. Here, an Adaptive and Attentive based TransDeeplabv3 + (AA-TransDeeplabv3 +)-based semantic segmentation model for input images is designed with a novel Hybridized Fire Hawk with Electric Fish Optimization (HFH-EFO). The parameters of Attentive-based TransDeeplabv3 + are tuned by developed HFH-EFO to attain the multi-objective function. The model is implemented using Python, which generates the empirical results. Therefore, the developed method achieves a dice coefficient of 93.02% and an accuracy value of 93.01%, outperforming traditionalapproaches. Hence, the proposedframework secures anexcellent result than the classical technique based on experimental analysis.</p>

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AA-TransDeeplabv3 + : a novel semantic segmentation framework for aerial images using adaptive and attentive based Transdeeplabv3 + with hybrid optimization technique

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • P. Anilkumar,
  • P. Venugopal,
  • K. Lokesh,
  • G. NagaJyothi,
  • M. Nanda kumar

摘要

Aerial imagesemantic segmentation is crucial for various operations such as military observation, land classification, and disaster impact assessments involving unmanned aerial vehicles. Although existing system is unsuited for aerial applications, these algorithms are mostly trained on human-centric datasets like “Cityscapes and Cam Vid”. High-resolution aerial image semantic segmentation is a basic and difficult task with several applications. Even though numerous Convolution Neural Network (CNN) segmentation techniques have shown impressive results, it is still challenging to discriminate semantic parts among regions with comparable spectral properties employing only high-resolution data. Additionally, the typical data-independent up-sampling techniques could produce poor outcomes. Thus, a novel semantic segmentation technique is introduced to resolve the complication presented in the classical segmentation framework in aerial images by utilizing deep learning techniques. Here, an Adaptive and Attentive based TransDeeplabv3 + (AA-TransDeeplabv3 +)-based semantic segmentation model for input images is designed with a novel Hybridized Fire Hawk with Electric Fish Optimization (HFH-EFO). The parameters of Attentive-based TransDeeplabv3 + are tuned by developed HFH-EFO to attain the multi-objective function. The model is implemented using Python, which generates the empirical results. Therefore, the developed method achieves a dice coefficient of 93.02% and an accuracy value of 93.01%, outperforming traditionalapproaches. Hence, the proposedframework secures anexcellent result than the classical technique based on experimental analysis.