利用无人机遥感图像进行基础设施轮廓分割的自适应图层选择和融合网络

Shuo Ma;Teng Li;Shuangshuang Zhai
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引用次数: 0

摘要

由于无人机的机动性和灵活性,基于无人机的遥感图像已被广泛应用于城市基础设施的监测,如建筑物、桥梁、水坝等。然而,随着无人机采集数据量的不断增加,由于数据量大、遥感图像分辨率高、建筑物形状不一致、建筑物与背景像素分布不平衡等原因,轮廓分割任务面临着挑战。为应对这些挑战,本文提出了一种基于自适应隐层特征融合的基础设施轮廓分割深度学习方法。它介绍了一种自适应层选择和融合网络(ALSFN),由编码器网络、自适应层选择机制(ALSM)和解码器网络组成。此外,这封信还提出了一种复合损失函数,其中包括边界评估和 Tversky 指数,用于训练所提出的神经网络。在真实无人机遥感数据集上进行的验证实验表明,所提出的方法在基础设施轮廓分割任务中实现了较高的准确性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Layer Selection and Fusion Network for Infrastructure Contour Segmentation Using UAV Remote Sensing Images
With the maneuverability and flexibility of UAVs, UAV-based remote sensing images have been widely applied for urban monitoring of infrastructure, such as buildings, bridges, dams, and so on. However, with the increasing amount of data collected by UAVs, challenges arise in contour segmentation tasks due to the large data volume, high resolution of remote sensing images, inconsistent building shapes, and imbalanced distribution of building and background pixels. To address these challenges, this letter proposes a deep learning method for infrastructure contour segmentation based on adaptive hidden-layer feature fusion. It introduces an adaptive layer selection and fusion network (ALSFN), consisting of an encoder network, an adaptive layer selection mechanism (ALSM), and a decoder network. Furthermore, this letter proposes a composite loss function that includes the evaluation of the boundary and the Tversky index to train the proposed neural network. Validation experiments conducted on real UAV remote sensing datasets show that the proposed method achieves high accuracy and reliability for infrastructure contour segmentation tasks.
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