Selvi Sundarapandi Arun Mozhi, Alotaibi Youseef, Thanarajan Tamilvizhi, Rajendran Surendran
Department of Computer Science and Engineering, Holycross Engineering College, Thoothukudi, 628851, India.
Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
Heliyon. 2024 Feb 21;10(5):e26589. doi: 10.1016/j.heliyon.2024.e26589. eCollection 2024 Mar 15.
Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas. Thus, the extraction of road network data in RSI is still a complex issue. In recent times, DL-based approaches have projected a famous development in image segmentation outcomes, but a lot of them still could not retain boundary data and attain high-resolution road segmentation maps while processing the RSI. Traditional convolutional neural networks (CNNs) demonstrate impressive performance in road extract tasks; however, they frequently encounter difficulties in capturing intricate details and contextual information. The study introduces a novel method, named Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), to tackle the challenges encountered in remote sensing images. The AOA-QDCNNRE technique aims to generate a high-resolution road segmentation map using DL with a hyperparameter tuning process. The AOA-QDCNNRE technique primarily relies on the QDCNN model, which integrates quantum technology (QC) with dilated convolutions to augment the network's capacity to capture local as well as global contextual information. In addition, the incorporation of the dilated convolutional technique effectively enhances the receptive field without sacrificing spatial resolution, enabling the extraction of precise road features. To develop the road extraction outcomes of the QDCNN approach, the AOA-based hyperparameter tuning process can be exploited. The AOA-QDCNNRE system's simulation results can be tested on benchmark databases, and the results indicate that the AOA-QDCNNRE method surpasses recent algorithms.
道路与人类生存紧密相连，提取道路网络的过程已成为遥感（RS）中最突出的任务。与传统的遥感图像（RSI）目视判读相比，遥感图像的自动道路判读过程能够以较低成本高效获取道路网络数据。然而，由于不同区域道路网络在长度、宽度、材质和形状上存在巨大差异，RSI的表现完全不同。因此，RSI中道路网络数据的提取仍然是一个复杂的问题。近年来，基于深度学习（DL）的方法在图像分割结果方面取得了显著进展，但其中许多方法在处理RSI时仍无法保留边界数据并获得高分辨率的道路分割图。传统卷积神经网络（CNN）在道路提取任务中表现出色；然而，它们在捕捉复杂细节和上下文信息时经常遇到困难。该研究引入了一种名为阿基米德优化算法-量子扩张卷积神经网络道路提取（AOA-QDCNNRE）的新方法，以应对遥感图像中遇到的挑战。AOA-QDCNNRE技术旨在通过超参数调整过程使用DL生成高分辨率的道路分割图。AOA-QDCNNRE技术主要依赖于QDCNN模型，该模型将量子技术（QC）与扩张卷积相结合，以增强网络捕捉局部和全局上下文信息的能力。此外，扩张卷积技术的引入有效地扩大了感受野，同时不牺牲空间分辨率，从而能够提取精确的道路特征。为了提高QDCNN方法的道路提取效果，可以利用基于AOA的超参数调整过程。AOA-QDCNNRE系统的仿真结果可以在基准数据库上进行测试，结果表明AOA-QDCNNRE方法优于最近的算法。
