MixerNet SAGA——一种新的深度学习架构,用于高分辨率遥感图像中的高级道路提取

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Wei Wu, Chao Ren, Anchao Yin, Xudong Zhang
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引用次数: 0

摘要

在本研究中,我们解决了当前深度学习模型在从遥感图像提取道路任务中的局限性。我们介绍了MixerNet-SAGA,这是一种新颖的深度学习模型,它结合了U-Net的优势,集成了用于增强特征提取的ConvMixer块,并包括用于增强空间注意力的缩放注意门(SAG)。在马萨诸塞州道路数据集和DeepGlobe道路数据集上的实验验证表明,与U-Net、ResNet和sduet等领先模型相比,MixerNet-SAGA的精度提高了10%,召回率提高了8%,IoU提高了12%。此外,我们的模型在计算效率方面表现出色,速度提高了20%,并且模型尺寸更小。值得注意的是,MixerNet-SAGA在应对同频谱不同对象和不同频谱相同对象现象等挑战时表现出了出色的鲁棒性。消融研究进一步揭示了ConvMixer块和SAG的关键作用。尽管有其优势,但该模型在超大数据集上的可扩展性仍然是未来研究的一个领域。总的来说,MixerNet-SAGA为遥感图像中的道路提取提供了高效、准确的解决方案,具有更广泛的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MixerNet-SAGA A Novel Deep Learning Architecture for Superior Road Extraction in High-Resolution Remote Sensing Imagery
In this study, we address the limitations of current deep learning models in road extraction tasks from remote sensing imagery. We introduce MixerNet-SAGA, a novel deep learning model that incorporates the strengths of U-Net, integrates a ConvMixer block for enhanced feature extraction, and includes a Scaled Attention Gate (SAG) for augmented spatial attention. Experimental validation on the Massachusetts road dataset and the DeepGlobe road dataset demonstrates that MixerNet-SAGA achieves a 10% improvement in precision, 8% in recall, and 12% in IoU compared to leading models such as U-Net, ResNet, and SDUNet. Furthermore, our model excels in computational efficiency, being 20% faster, and has a smaller model size. Notably, MixerNet-SAGA shows exceptional robustness against challenges such as same-spectrum–different-object and different-spectrum–same-object phenomena. Ablation studies further reveal the critical roles of the ConvMixer block and SAG. Despite its strengths, the model’s scalability to extremely large datasets remains an area for future investigation. Collectively, MixerNet-SAGA offers an efficient and accurate solution for road extraction in remote sensing imagery and presents significant potential for broader applications.
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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