CRU-Net: An Innovative Network for Building Extraction From Remote Sensing Images Based on Channel Enhancement and Multiscale Spatial Attention With ResNet

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Zhuozhao Chen, Wenbo Chen, Jiao Zheng, Yuanyuan Ding
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引用次数: 0

Abstract

Building extraction from high-resolution remote sensing images enables systematic quantification of urban form evolution, supports critical decision-making in infrastructure development and land-use optimization, and facilitates disaster resilience and risk assessment. However, the characteristics of urban landscapes, such as high-density building distribution and heterogeneous building geometries, pose significant challenges in achieving pixel-level accuracy. To address these challenges, we proposed an innovative network for building extraction based on channel enhancement and multiscale spatial attention with ResNet (CRU-Net). First, CRU-Net employed U-Net as the core architecture, with ResNet34 as the encoder component. Second, to fully exploit the ability of convolutional neural networks to extract features at multiple scales, a new dilated residual block (DRB) was designed by combining a residual block with dilated convolution. Replacing the residual blocks in ResNet34 with DRB enhances the ability of CRU-Net to extract semantic information at different scales for building extraction. Next, the channel enhancement and multiscale spatial attention (CEMS) module was proposed and added to the skip connection of the network. CEMS is capable of learning more important features both spatially and channel-wise, enhancing the network's feature representation ability. Finally, a joint loss function combining normalized cross-correlation loss and binary cross-entropy loss was introduced to train the network, enabling it to focus on learning both global and local features of the building. The experiments show that CRU-Net achieves high accuracy and intersection over union (IoU) values on the Massachusetts building dataset, Inria aerial image labeling dataset, and WHU building dataset.

基于通道增强和多尺度空间关注的遥感影像建筑提取网络CRU-Net
从高分辨率遥感图像中提取建筑物可以系统地量化城市形态演变,支持基础设施发展和土地利用优化中的关键决策,并促进抗灾能力和风险评估。然而,城市景观的特点,如高密度的建筑分布和异质的建筑几何形状,对实现像素级精度提出了重大挑战。为了解决这些问题,我们提出了一种基于通道增强和多尺度空间关注的基于ResNet (crun - net)的创新建筑提取网络。首先,cr - net采用U-Net作为核心架构,以ResNet34作为编码器组件。其次,为了充分利用卷积神经网络在多尺度上提取特征的能力,将残差块与扩展卷积相结合,设计了一种新的扩展残差块(expanded residual block, DRB)。用DRB代替ResNet34中的残差块,增强了CRU-Net提取不同尺度语义信息的能力,用于建筑提取。其次,提出了信道增强和多尺度空间注意(CEMS)模块,并将其添加到网络的跳接中。CEMS能够在空间和信道上学习更重要的特征,增强网络的特征表示能力。最后,引入归一化互相关损失和二值交叉熵损失相结合的联合损失函数对网络进行训练,使其能够集中学习建筑物的全局特征和局部特征。实验表明,crun - net在Massachusetts建筑数据集、Inria航拍图像标注数据集和WHU建筑数据集上取得了较高的精度和IoU值。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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