PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing

Ziwen Zhang, Qi Liu, Xiaodong Liu, Yonghong Zhang, Zihao Du, Xuefei Cao
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Abstract

In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.
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PMNet:利用边缘云计算从高分辨率遥感图像中提取水的多分支和多尺度语义分割方法
在遥感图像解译领域,从高分辨率图像中自动提取水体信息是一项关键任务。然而,面对高分辨率遥感图像中复杂的多尺度特征,传统方法和基本的深度卷积神经网络难以有效捕捉目标对象的全局空间关系,导致提取的水体信息不完整、形状粗糙、边缘模糊。同时,海量图像数据处理通常会导致计算资源过载,效率低下。幸运的是,边缘计算的本地数据处理能力与云中心强大的计算资源相结合,可以为高分辨率遥感图像分割提供及时高效的计算和存储。为此,本文提出了一种用于边缘-云协作的轻量级深度学习网络--PMNet,该网络利用流水线式多步骤聚合方法捕捉不同尺度的图像信息,并通过水平和垂直空间维度理解遥感像素之间的关系。同时,它在解码阶段采用了多个解码分支的组合,而不是传统的单一解码分支。在降低系统资源消耗的同时,提高了结果的准确性。该模型在 Landsat-8 和 GID 遥感图像数据集上获得了 90.22 和 88.57 的 F1 分数,模型复杂度较低,优于其他语义分割模型,凸显了移动边缘计算在处理海量高分辨率遥感图像数据方面的潜力。
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