Strip Attention Networks for Road Extraction

Huan, Hai; Yu, Sheng; Zhang, Yi; Liu, Yuan

doi:10.3390/rs14184516

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…The former introduces the SPP module into the intermediate layers, while the latter uses the ASPP module. Huan et al [ 55 ] introduced the SANet model pre-trained with ResNet-50 and introduced the ASPP module in the encoder. Inspired by dense convolution, Q. Wu et al [ 56 ] introduced the dense and global spatial pyramid pooling module (DGSPP) into the decoder and encoder to enhance the network’s perception and aggregation of contextual information.…”

Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

“…Ensuring the maximum transmission of road information between dense blocks and coordinating multi-scale road information acquisition through the global attention module SANet [55] Strip Attention (SAM) Facilitating the fusion of lower-level and higher-level road features FE-LinkNet [59] Criss-Cross Attention (CCA)…”

Section: Feature Fusion Based On Attention Mechanismsmentioning

confidence: 99%

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A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

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Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

Section: Feature Fusion Based On Attention Mechanismsmentioning

confidence: 99%

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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“…The training batch size is set to 2. Since the experimental datasets have different GSDs, we choose different scaling rates for them in practical operations based on previous experience [36,125]. The minimum down-sampling rates for the dataset of Daxing District, Beijing, and the DeepGlobe dataset are 1/8 and 1/16, respectively.…”

Section: Experimental Settingsmentioning

confidence: 99%

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

et al. 2023

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Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss (LSSIM) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs.

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“…In the swiftly advancing field of remote sensing technology, we now have unprecedented access to high-resolution images of the Earth's surface, offering invaluable insights into geography [1], ecology [2] and urbanization [3]. Despite these advances, the processing of high-resolution remote sensing images presents myriad challenges, particularly in applications like road extraction, building detection, and land use classification [4,5].…”

Section: Introduction 1related Workmentioning

confidence: 99%

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

Xue,

Ren,

Yin

et al. 2024

Applied Sciences

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In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized performance. Initially, the Pyramid Pathway Input equips the model to identify features at multiple scales, markedly enhancing its ability to discriminate between roads and other background elements. Secondly, by adopting CoordConv convolutional layers, the model achieves heightened accuracy in road recognition and extraction against complex backdrops. Moreover, the DCA module serves dual purposes: it is employed at the encoder stage to efficiently consolidate feature maps across scales, thereby fortifying the model’s road detection capabilities while mitigating false positives. In the skip connection stages, the DCA module further refines the continuity and accuracy of the features. Extensive empirical evaluation substantiates that PCCAU-Net significantly outperforms existing state-of-the-art techniques on multiple benchmarks, including precision, recall, and Intersection-over-Union(IoU). Consequently, PCCAU-Net not only represents a considerable advancement in road extraction research, but also demonstrates vast potential for broader applications, such as urban planning and traffic analytics.

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Strip Attention Networks for Road Extraction

Cited by 10 publications

References 36 publications

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

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