The DOI matches its Crossref record; 23 of the 27 references it cites were found in a registry or an index.
Road Extraction by Deep Residual U-Net
Open accessAbstract
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model are twofold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters, however, better performance. We test our network on a public road data set and compare it with U-Net and other two state-of-the-art deep-learning-based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.
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“In this study, we opt for a deep residual U-Net [15], [44], which has already been successfully used for retinal OCT images.”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [44]
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 5×
“This somehow shares similar idea to that of residual neural network [21].”
BackgroundMethodResultInfluentialVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“Recently, lots of works have suggested that a deeper network would have better performance [19], [20].”
BackgroundU-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · cited 7×
“1) U-Net: In semantic segmentation, to get a finer result, it is very important to use low-level details, while retaining high-level semantic information [23], [24].”
BackgroundMethodInfluentialFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472 · cited 1×
“of-the-art performance on a variety of computer vision tasks, such as scene recognition [13] and object detection [14].”
BackgroundFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · cited 1×
“Recently, lots of works have suggested that a deeper network would have better performance [19], [20].”
BackgroundFully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 3×
“One way to solve this problem is employing a pretrained network and then finetuning it on the target data set, as done in [23].”
BackgroundMethodIdentity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · cited 2×
“[22] presented a detailed discussion on impacts of different combinations and suggested a full preactivation design as shown in Fig.”
BackgroundMethodDeep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,272
Learning to Detect Roads in High-Resolution Aerial Images
Mnih, V., & Hinton, G. E. (2010) · European Conference on Computer Vision · doi:10.1007/978-3-642-15567-3_16 · Cited by 681 · cited 11×
“To demonstrate the accuracy and efficiency of the proposed deep ResUnet, we test it on Massachusetts roads data set2 and compare it with three state of the art methods, including Mnih’s [2] method, Saito’s method [5], and U-Net [24].”
BackgroundMethodResultInfluentialCheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., & Pan, C. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2669341 · Cited by 439 · cited 1×
“There are also methods that extract both road areas and centerline, simultaneously [9].”
MethodSelf-citationRoad Extraction Using SVM and Image Segmentation
Song, M., & Civco, D. L. (2004) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.70.12.1365 · Cited by 303 · cited 1×
“For instance, Song and Civco [11] proposed a method utilizing shape index feature and support vector machine (SVM) to detect road areas.”
MethodDas, S., Mirnalinee, T. T., & Varghese, K. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2136381 · Cited by 232 · cited 1×
“[12] exploited two salient features of roads and designed a multistage framework to extract roads”
BackgroundMultiple Object Extraction from Aerial Imagery with Convolutional Neural Networks
Saito, S., Yamashita, T., & Aoki, Y. (2016) · IRIACV · doi:10.2352/issn.2470-1173.2016.10.robvis-392 · Cited by 213 · cited 5×
“[5], U-Net [24], and the proposed ResUnet.”
BackgroundMethodResultInfluentialRoad Network Detection Using Probabilistic and Graph Theoretical Methods
Unsalan, C., & Sirmaçek, B. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2190078 · Cited by 172
Huang, X., & Zhang, L. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160802546837 · Cited by 153 · cited 1×
“Road area extraction [1]–[6] can generate pixel-level labeling of roads, while road centerline extraction [7], [8] aims at detecting skeletons of a road.”
BackgroundAlshehhi, R., & Marpu, P. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.02.008 · Cited by 150 · cited 3×
“Manuscript received **, 2017; revised **, 2017. multi-spectral images using probabilistic SVM. Alshehhi and Marpu [6] proposed an unsupervised road extraction method based on hierarchical graph-based image segmentation.”
BackgroundSujatha, C., & Selvathi, D. (2015) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-015-0062-9 · Cited by 78 · cited 1×
“Road area extraction [1]–[6] can generate pixel-level labeling of roads, while road centerline extraction [7], [8] aims at detecting skeletons of a road.”
BackgroundCNN based suburban building detection using monocular high resolution Google Earth images
Zhang, Q., Wang, Y., Liu, Q., Liu, X., & Wang, W. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729166 · Cited by 64
Self-citationRoad Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression
Cheng, G.-L., Zhu, F.-Y., Xiang, S., & Pan, C.-H. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2524025 · Cited by 59 · cited 1×
“Since road centerline can be easily obtained from road areas using algorithms such as morphological thinning [10], this letter focuses on road area extraction from high-resolution remote sensing images.”
Liu, B., Wu, H.-Y., Wang, Y.-D., & Liu, W.-M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138071 · Cited by 43 · cited 1×
“Road area extraction [1]–[6] can generate pixel-level labeling of roads, while road centerline extraction [7], [8] aims at detecting skeletons of a road.”
BackgroundSelf-citationUrban road extraction via graph cuts based probability propagation
Cheng, G.-L., Wang, Y., Gong, Y., Zhu, F.-Y., & Pan, C.-H. (2014) · International Conference on Information Photonics · doi:10.1109/icip.2014.7026027 · Cited by 29
Self-citationA CNN based functional zone classification method for aerial images
Zhang, Z.-X., Wang, Y.-H., Liu, Q., Li, L., & Wang, P. (2016) · IEEE International Geoscience and Remote Sensing Symposium · doi:10.1109/igarss.2016.7730419 · Cited by 21 · cited 1×
“lems of interpretation and understanding of remote sensing data [2], [5], [15]–[18].”
BackgroundSelf-citation
Flagged first, then by citations
4 of the 27 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- zhou, 2014, Learning deep features for scene recognition using places database, Proc NIPS, 487
- mnih, 2012, Learning to label aerial images from noisy data, Proc ICML, 567
- ehrig, 2005, Relaxed precision and recall for ontology matching, Proc Workshop Integr Ontol, 25
- chollet, 2015, Keras
Cited by
Sorted by citations
Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
“[49] developed a road segmentation algorithm based on U-Net, and Cicek et al.”
MethodKvasir-SEG: A Segmented Polyp Dataset
Jha, D., Smedsrud, P., Riegler, M., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. (2019) · Conference on Multimedia Modeling · doi:10.1007/978-3-030-37734-2_37 · Cited by 2,145
“conducted initial investigations using two dierent methods. The rst method is based on the ecient FCM [6] unsupervised clustering algorithm. The second method is based on the deep-learning ResUNet [28] architecture, utilizing the advantage of the residual block. When using basic CNN architectures to predict outcomes i…”
MethodInfluentialResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Diakogiannis, F. I., Waldner, F., Caccetta, P. A., & Wu, C. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.013 · Cited by 2,295
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J.-B., & Shao, L. (2020) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-59725-2_26 · Cited by 2,003
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Zhang, Y., Liu, H., & Hu, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_2 · Cited by 1,694
Metadata
- Authors
- Zhang, Z., Liu, Q., & Wang, Y.
- Affiliations
- State Key Laboratory of Virtual Reality Technology and Systems (CN)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2018-05
- Volume
- 15
- Issue
- 5
- Pages
- 749-753
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2774320778
- Cited by
- 3,179
- References
- 27
- Citations by year
- 2026: 331 · 2025: 526 · 2024: 616 · 2023: 510 · 2022: 441 · 2021: 342 · 2020: 241 · 2019: 147 · 2018: 25
- FWCI
- 151.19
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Natural Science Foundation of China (61601011)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- road extraction, aerial images, semantic segmentation, residual learning, U-Net, skip connections
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