标题： 深度神经变换器和卷积神经网络在现代遥感数据集上的评估与分析 显示英文标题

标题： Evaluation and Analysis of Deep Neural Transformers and Convolutional Neural Networks on Modern Remote Sensing Datasets

摘要： 2012年，AlexNet将深度卷积神经网络（DCNNs）确立为计算机视觉的最先进方法，因为这些网络很快在许多领域的视觉任务中取得了领先，包括遥感。 随着视觉变压器的发表，我们正在见证计算视觉的第二次现代飞跃，因此，理解各种基于变压器的神经网络在卫星图像上的表现是至关重要的。 虽然变压器在自然语言处理和计算机视觉应用中表现出高水平的性能，但它们尚未与现代遥感数据进行大规模比较。 在本文中，我们探讨了基于变压器的神经网络在高分辨率光电卫星图像中目标检测的应用，并在多种公开可用的基准数据集上展示了最先进的性能。 在这项研究中，我们比较了十一种不同的边界框检测和定位算法，其中七种是在2020年后发表的，所有十一种都是在2015年后发表的。 在三个最先进的开源高分辨率遥感图像数据集上，比较了五种基于变压器的架构与六种卷积网络的性能，这些数据集在规模和复杂性上各不相同。 在训练和评估了三十三个深度神经模型之后，我们随后讨论并分析了不同特征提取方法和检测算法下的模型性能。 显示英文摘要

摘要： In 2012, AlexNet established deep convolutional neural networks (DCNNs) as the state-of-the-art in CV, as these networks soon led in visual tasks for many domains, including remote sensing. With the publication of Visual Transformers, we are witnessing the second modern leap in computational vision, and as such, it is imperative to understand how various transformer-based neural networks perform on satellite imagery. While transformers have shown high levels of performance in natural language processing and CV applications, they have yet to be compared on a large scale to modern remote sensing data. In this paper, we explore the use of transformer-based neural networks for object detection in high-resolution electro-optical satellite imagery, demonstrating state-of-the-art performance on a variety of publicly available benchmark data sets. We compare eleven distinct bounding-box detection and localization algorithms in this study, of which seven were published since 2020, and all eleven since 2015. The performance of five transformer-based architectures is compared with six convolutional networks on three state-of-the-art opensource high-resolution remote sensing imagery datasets ranging in size and complexity. Following the training and evaluation of thirty-three deep neural models, we then discuss and analyze model performance across various feature extraction methodologies and detection algorithms.