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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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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].”
BackgroundMethodResultInfluential
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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
BASNet: Boundary-Aware Salient Object Detection
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., & Jägersand, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00766 · Cited by 1,691
“In our experiments, we set the slack parameter ρ to 3 similar to the previous studies [44, 58, 77].”
ResultSelf-citationResUNet++: An Advanced Architecture for Medical Image Segmentation
Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., de Lange, T., Halvorsen, P., & Johansen, H. D. (2019) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism46123.2019.00049 · Cited by 1,627
“There are various modification and extension based on U-Net architecture [4], [6], [17], [19]–[22] to achieve better segmentation results on both natural images and biomedical images.”
BackgroundMethodInfluentialAlom, M. Z., Hasan, M. M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06955 · Cited by 909
“However, U-Net is not only limited to the applications in the domain of medical imaging, nowadays this model is massively applied for computer vision tasks as well [30, 31].”
MethodPlug-and-Play Image Restoration With Deep Denoiser Prior
Zhang, K., Li, Y., Zuo, W., Lei Zhang, A., Gool, L. V., & Timofte, R. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3088914 · Cited by 1,152
“Actually, the similar idea of combining U-Net and ResNet can also be found in other works such as [61], [62].”
ResultTransBTS: Multimodal Brain Tumor Segmentation Using Transformer
Wang, W., Chen, C., Ding, M., Li, J., Yu, H., & Zha, S. (2021) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-87193-2_11 · Cited by 1,278
“as U-Net++ [21] and Res-UNet [20] further improve the performance for image segmentation.”
A survey on deep learning techniques for image and video semantic segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., Martínez-González, P., & García‐Rodríguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 2,646
Medical Image Segmentation Review: The Success of U-Net
Azad, R., Aghdam, E. K., Rauland, A., Jia, Y., Avval, A. H., Bozorgpour, A., Karimijafarbigloo, S., Cohen, J., Adeli, E., & Merhof, D. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435571 · Cited by 1,280
“Residual U-Net [173], even with the Residual enhancements, is no exception to this rule.”
BackgroundMethodUNeXt: MLP-based Rapid Medical Image Segmentation Network
Valanarasu, J. M. J., & Patel, V. M. (2022) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.48550/arxiv.2203.04967 · Cited by 1,075
“In particular, we compare with convolutional baselines like UNet [17], UNet++ [29] and ResUNet [28].”
MethodDS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
Lin, A., Chen, B., Xu, J., Zhang, Z., Lu, G., & Zhang, D. D. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3178991 · Cited by 926
Self-citationCOVID-CT-Dataset: A CT Scan Dataset about COVID-19Preprint
Yang, X., Zhao, J.-Y., Zhang, Y.-C., He, X.-H., & Xie, P.-T. (2020) · arXiv.org · doi:10.48550/arxiv.2003.13865 · Cited by 926
“Using the labeled lung masks in COVID-CT and COVID-Seg, we develop a lung segmentation model based on residual U-Net (Zhang et al., 2018).”
MethodZhou, L., Zhang, C., & Wu, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00034 · Cited by 1,035
“Most of these methods can be seperated into three categories: generating pixel-level labeling of roads [1, 2], detecting skeletons of roads [3, 4] and a combination of both [5, 6].”
MethodDoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Jha, D., Riegler, M., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00111 · Cited by 998
“[15] proposed Deep Residual U-Net, which uses residual connections better output segmentation map.”
Recurrent residual U-Net for medical image segmentation
Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.014006 · Cited by 906
“The proposed RU-Net and R2U-Net architectures are designed to have the same number of network parameters, when compared to U-Net and ResU-Net, and the RU-Net and R2U-Net models show better performance on segmentation tasks.”
MethodInfluentialA review of deep learning methods for semantic segmentation of remote sensing imagery
Yuan, X., Shi, J., & Gu, L. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114417 · Cited by 832
“Xu, Du, Zhang, and Zhang (2018) proposed a Random Patches Network that takes image patches as the convolution kernels, which requires no training.”
BackgroundFAT-Net: Feature adaptive transformers for automated skin lesion segmentation
Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., & Wen, Z. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102327 · Cited by 617
A global inventory of photovoltaic solar energy generating units
Kruitwagen, L., Story, K. T., Friedrich, J., Byers, L., Skillman, S. W., & Hepburn, C. J. (2021) · Nature · doi:10.1038/s41586-021-03957-7 · Cited by 502
DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Xu, Q., Ma, Z., He, N., & Duan, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106626 · Cited by 476
“The VGGNet [39] and typical residual structures [23] have been applied in many previous semantic segmentation networks, such as DoubleUnet [22] and ResUnet [40].”
MethodShallow Attention Network for Polyp Segmentation
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S., & Cui, S. (2021) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-87193-2_66 · Cited by 447
“Besides, six state-of-the-art methods are used for comparison, namely U-Net [15], U-Net++ [25], ResUNet [23], ResUNet++ [11], SFA [6] and PraNet [5].”
BackgroundMethodVali, A., Comai, S., & Matteucci, M. (2020) · Remote Sensing · doi:10.3390/rs12152495 · Cited by 507
“In the context of remote sensing, U-Net has shown very promising results in extracting buildings [127,128], roads [129,130], clouds [131,132], and to classify other land covers [133–135] using high resolution MSI data.”
BackgroundTolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., et al. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113888 · Cited by 419
“We utilized a ResUNet-18 architecture for our baseline (Zhang et al., 2017), which is an encoder-decoder architecture predicting a N × N canopy height map from a 3 × N × N RGB image, with N = 256.”
MethodA cloud detection algorithm for satellite imagery based on deep learning
Jeppesen, J. H., Jacobsen, R. H., Inceoglu, F., & Toftegaard, T. S. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.03.039 · Cited by 402
Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024) · Bioengineering · doi:10.3390/bioengineering11101034 · Cited by 393
Deep learning for topology optimization of 2D metamaterials
Kollmann, H. T., Abueidda, D., Koric, S., Guleryuz, E., & Sobh, N. (2020) · Materials & Design · doi:10.1016/j.matdes.2020.109098 · Cited by 343
CaraNet: context axial reverse attention network for segmentation of small medical objects
Lou, A., Guan, S., & Loew, M. H. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.1.014005 · Cited by 160
“We compare our CaraNet with six state-of-the-art medical image segmentation methods: UNet [1], U-Net++ [2], ResUNetmod [15], ResUNet++ [3], SFA [16] and PraNet [4].”
MethodJha, D., Smedsrud, P. H., Johansen, D., de Lange, T., Johansen, H. D., Halvorsen, P., & Riegler, M. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3049304 · Cited by 350
“In our previous work, we have showed that ResUNet++ outperforms the SOTA UNet [35] and ResUNet [34] models trained on Kvasir-SEG and CVC-ClinicDB dataset [1].”
MethodCS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging
Mou, L., Zhao, Y., Fu, H., Liu, Y., Cheng, J. T., Zheng, Y., Su, P., Yang, J., Chen, L., Frangi, A. F., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101874 · Cited by 453
“, 2015), Deep ResUNet (Zhang et al., 2018), U-Net++ (Zhou et al.”
InfluentialINet: Convolutional Networks for Biomedical Image Segmentation
Weng, W., & Zhu, X. (2021) · IEEE Access · doi:10.1109/access.2021.3053408 · Cited by 569
“Compared to the commonly used baseline model, UNet, the proposed INet has additional residual shortcuts, and therefore the learning task of the optimizer becomes easier [39] (We denote INet without customized residual short-cuts and the Conv-index as Plain-INet).”
MethodROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
Ma, Y., Hao, H., Xie, J., Fu, H., Zhang, J., Yang, J., Wang, Z., Liu, J., Zheng, Y., & Zhao, Y. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3042802 · Cited by 334
“[25] modified ResU-Net [26] by introducing a weighted attention mechanism for high-quality retinal vessel segmentation.”
BackgroundMethodInfluentialReal-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Jha, D., Ali, S., Tomar, N. K., Johansen, H. D., Johansen, D., Rittscher, J., Riegler, M. A., & Halvorsen, P. (2021) · IEEE Access · doi:10.1109/access.2021.3063716 · Cited by 423
“ResUNet [62] integrates the power of both UNet and residual neural network.”
MethodMSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Srivastava, A., Jha, D., Chanda, S., Pal, U., Johansen, H., Johansen, D., Riegler, M. A., Ali, S., & Halvorsen, P. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3138024 · Cited by 369
“We report a mIoU of 0.9043 ± 0.1009 and a recall of 0.9567 ± 0.0620, which is 1.44% improvement in mIoU and 2.82% improvement in recall over SOTA combination of ResUNet++ and conditional random field [57] and UACANet-S [35], respectively.”
MethodInfluentialA Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery
Zhu, Q., Zhang, Y., Wang, L., Zhong, Y., Guan, Q., Lu, X., Zhang, L., & Li, D. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.03.016 · Cited by 314
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328
CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation
Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z.-W., Hao, H., Qi, H., Zheng, Y., Frangi, A. F., & Liu, J. (2019) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-32239-7_80 · Cited by 296
Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network
Horng, M.-H., Kuok, C.-P., Fu, M.-J., Lin, C.-J., & Sun, Y.-N. (2019) · Comput. Math. Methods Medicine · doi:10.1155/2019/6357171 · Cited by 296
“-ese results reveal that the performance of our proposed work is very close to the one in [24] and should be better than the abovementioned methods [21–23].”
MethodResultSwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer
Jiang, Y., Zhang, Y., Lin, X., Dong, J.-K., Cheng, T., & Liang, J. (2022) · Brain Science · doi:10.3390/brainsci12060797 · Cited by 291
de Bem, P. P., Júnior, O. A. D. C., Guimarães, R. F., & Gomes, R. A. T. (2020) · Remote Sensing · doi:10.3390/rs12060901 · Cited by 299
“This research used three different DL architectures available in the literature: U-Net [79], SharpMask [80], and ResUnet [81].”
MethodAbdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., & Alamri, A. M. (2020) · Remote Sensing · doi:10.3390/rs12091444 · Cited by 299
“[60] developed a deep residual U-net model similar to a U-net architecture for road semantic segmentation from high-resolution remote sensing imagery.”
MethodRoad Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning
Xu, Y., Xie, Z., Feng, Y., & Chen, Z. (2018) · Remote Sensing · doi:10.3390/rs10091461 · Cited by 300
“Zhang et al. combined deep residual networks (ResNet) [32] and U-Net [33], which allow for networks to be designed with fewer parameters, but obtain better results [4].”
BackgroundResultFast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
Ji, G.-P., Zhu, L., Zhuge, M., & Fu, K. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108414 · Cited by 247
“Note that “ † ” represents evaluation scores from [73] and “N/A” means the result is not available. tion methods: UNet [67], UNet++ [22], ResUNet-mod [75], ResUNet++ [73], SFA [72] and PraNet [13].”
MethodCoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery
Mei, J., Li, R., Gao, W., & Cheng, M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3117076 · Cited by 247
“[22] propose a semantic segmentation neural network called ResUnet, which is combined with residual learning and U-Net for road area extraction.”
BackgroundMethodImproved Road Connectivity by Joint Learning of Orientation and Segmentation
Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C. V., & Paluri, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01063 · Cited by 244
An RDAU-NET model for lesion segmentation in breast ultrasound images
Zhuang, Z., Li, N., Raj, A. N. J., Mahesh, V. G. V., & Qiu, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221535 · Cited by 240
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Tomar, N. K., Jha, D., Riegler, M. A., Johansen, H. D., Johansen, D., Rittscher, J., Halvorsen, P., & Ali, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3159394 · Cited by 263
Left-Ventricle Quantification Using Residual U-Net
Kerfoot, E., Clough, J. R., Öksüz, İ., Lee, J., King, A. P., & Schnabel, J. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12029-0_40 · Cited by 244
PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media
Santos, J. E., Xu, D., Jo, H., Landry, C. J., Prodanović, M., & Pyrcz, M. J. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103539 · Cited by 252
FCN-Transformer Feature Fusion for Polyp Segmentation
Sanderson, E., & Matuszewski, B. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_65 · Cited by 237
“U-Net [25] 0.7821 0.8141 0.7241 0.8450 0.8464 0.7730 0.8496 0.8796 ResUNet [38] 0.5133 0.3792 0.5937 0.5968 0.5221 0.4120 0.6151 0.5895 ResUNet++ [17] 0.8074 0.7231 0.8991 0.7874 0.5211 0.4126 0.5633 0.5693 MSRF-Net [28] 0.8586 0.7906 0.8933 0.8774 0.9198 0.8729 0.9222 0.9308 PraNet [8] 0.9011…”
BackgroundMethodInfluentialEdge U-Net: Brain Tumor Segmentation using MRI based on Deep U-Net Model with boundary information
Allah, A. M. G., Sarhan, A. M., & Elshennawy, N. M. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.118833 · Cited by 215
Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., & Zhao, T. (2019) · Remote Sensing · doi:10.3390/rs11151774 · Cited by 237
“Although some combined approaches of the U-Net with the deep residual network have been reported in recent studies [51,57,58], significant differences can be found between their network architectures and that of ours.”
BackgroundSelf-citationStructure and Illumination Constrained GAN for Medical Image Enhancement
Ma, Y., Liu, J., Liu, Y., Fu, H., Hu, Y., Cheng, J. T., Qi, H., Wu, Y., Zhang, J., & Zhao, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3101937 · Cited by 231
Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net
Yang, X., Li, X., Ye, Y., Lau, R. Y. K., Zhang, X., & Huang, X. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2912301 · Cited by 227
“Although spurs are rarely seen, discontinuities and false positive parts still appear in the results produced by ResUNet.”
BackgroundMethodInfluentialLandslide detection using deep learning and object-based image analysis
Ghorbanzadeh, O., Shahabi, H., Crivellari, A., Homayouni, S., Blaschke, T., & Ghamisi, P. (2022) · Landslides · doi:10.1007/s10346-021-01843-x · Cited by 205
“Each has a batch normalization layer and a ReLU activation layer in advance (Liu et al. 2020; Zhang et al. 2018).”
BackgroundMethodInfluentialTopology optimization of 2D structures with nonlinearities using deep learning
Abueidda, D., Korić, S., & Sobh, N. (2020) · Computers & Structures · doi:10.1016/j.compstruc.2020.106283 · Cited by 234
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
Ghorbanzadeh, O., Xu, Y., Ghamisi, P., Kopp, M., & Kreil, D. P. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3215209 · Cited by 248
“This variant of the U-Net features structures based on residual learning blocks that are replaced with the plain convolution layers [86].”
BackgroundRoad Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks
Henry, C., Azimi, S., & Merkle, N. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2864342 · Cited by 202
“This letter presents the evaluation of three FCNNs for road segmentation in high-resolution SAR satellite images: FCN-8s [4], Deep Residual U-Net [5], and DeepLabv3+ [9].”
BackgroundMethodLin, J.-W., Lin, J., Lu, C., Chen, H., Lin, H., Zhao, B.-C., Shi, Z.-W., Qiu, B., Pan, X., Xu, Z.-Y., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3250474 · Cited by 200
“We compare our proposed CKD-TransBTS model with several SOTA models, including five CNN-based models ( VNet [45], ResUNet [46], UNet++ [47] AttentionUNet [48] and DynUNet [20]) and six transformer-based models (Trans-BTS [28], TransUNet [27], UNETR [7], VTNet [30], Seg-TransVAE [49] and Swin UNETR [32]).”
MethodTang, Y.-C., Huang, Z.-F., Chen, Z., Chen, M.-Y., Zhou, H., Zhang, H.-X., & Sun, J.-B. (2023) · Engineering structures · doi:10.1016/j.engstruct.2022.115158 · Cited by 198
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Azad, R., Heidari, M., Shariatnia, M., Aghdam, E. K., Karimijafarbigloo, S., Adeli, E., & Merhof, D. (2022) · PRIME@MICCAI · doi:10.48550/arxiv.2208.00713 · Cited by 197
“Following this technical route, many U-Net variants such as U-Net++ [37] and Res-UNet [36] have emerged to improve the segmentation performance.”
MethodInfluentialRoad extraction in remote sensing data: A survey
Chen, Z., Deng, L., Luo, Y., Li, D., Marcato, J., Gonçalves, W. N., Nurunnabi, A., Li, J., Wang, C., & Li, D. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102833 · Cited by 197
Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation
Zhu, Z.-Q., Sun, M.-W., Qi, G.-Q., Li, Y., Gao, X.-B., & Liu, Y. (2024) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2024.108284 · Cited by 195
Highly Accurate Dichotomous Image Segmentation
Qin, X., Dai, H., Hu, X.-B., Fan, D.-P., Shao, L., & Gool, L. V. (2022) · European Conference on Computer Vision · doi:10.48550/arxiv.2203.03041 · Cited by 192
“For example, IoU and F-measure mainly rely on the object components with large areas while neglecting the fine details with relatively small areas.”
BackgroundInfluentialA multi-centre polyp detection and segmentation dataset for generalisability assessment
Ali, S., Jha, D., Ghatwary, N., Realdon, S., Cannizzaro, R., Salem, O. E., Lamarque, D., Daul, C., Riegler, M., Ånonsen, K., et al. (2021) · Scientific Data · doi:10.1038/s41597-023-01981-y · Cited by 192
R oot P ainter : deep learning segmentation of biological images with corrective annotation
Smith, A. G., Han, E., Petersen, J., Olsen, N. A. F., Giese, C., Athmann, M., Dresbøll, D. B., & Thorup‐Kristensen, K. (2022) · New Phytologist · doi:10.1111/nph.18387 · Cited by 225
“Roots Dataset We downloaded the 867 grid counted images and manual root length measurements from (67) which were made available as part of the evaluation of U-Net for segmenting roots in soil (40) and originally captured as part of a study on chicory drought stress (4) using a 4 m rhizobox laboratory described in (68).”
MethodInfluentialCrackFormer Network for Pavement Crack Segmentation
Liu, H., Yang, J., Miao, X., Mertz, C., & Kong, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3266776 · Cited by 201
“example, UNet [20], [21] and FusionNet [22] usually for medical images segmentation can be used to pixel-level crack”
MethodZhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., & Zhang, S. (2018) · Sensors · doi:10.3390/s18113717 · Cited by 220
“It combines low level features with detailed spatial information with high level features with semantic information to improve segmentation accuracy, and has attained promising results in one-class segmentation tasks such as biomedical image segmentation [30], road network extraction from aerial imagery [31,32], build…”
BackgroundSelf-citationUNet-based model for crack detection integrating visual explanations
Liu, F., & Wang, L. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126265 · Cited by 194
Abdollahi, A., Pradhan, B., & Alamri, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3026658 · Cited by 196
MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation
Su, R. Z., Zhang, D., Liu, J., & Cheng, C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.639930 · Cited by 177
“Zhang et al. (2018) proposed a semantic segmentation neural network based on residual learning and U-Net for road area extraction.”
BackgroundHSNet: A hybrid semantic network for polyp segmentation
Zhang, W., Fu, C., Zheng, Y., Zhang, F., Zhao, Y., & Sham, C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106173 · Cited by 165
Wang, Q., Huang, W. F., Xiong, Z., & Li, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3042276 · Cited by 192
“BENEFITING from the remote sensing imaging equipments and technologies, in recent years, many semanticlevel tasks of remote sensing images have developed rapidly, such as object detection [1], image retrieval [2], image captioning [3] [4], road extraction [5] and the others.”
BackgroundLi, C., Tan, Y., Chen, W., Luo, X., He, Y., Gao, Y., & Li, F. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.05.003 · Cited by 178
Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
Buslaev, A. P., Seferbekov, S., Iglovikov, V., & Shvets, A. A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00035 · Cited by 186
“This type of network architecture proven themselves in binary image segmentation competitions for satellite image analyses [6, 7, 8, 15, 16] and other applications [13, 12].”
BackgroundMulti-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction
Lu, X., Zhong, Y., Zheng, Z., Liu, Y., Zhao, J. J., Ma, A., & Yang, J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2926397 · Cited by 196
“In recent years, various methods have been applied to road extraction, which can be divided into two types: road detection methods [6]–[8] and road centerline extraction methods [9]–[12].”
BackgroundMethodInfluentialZhou, G., Chen, W., Gui, Q., Li, X., & Wang, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3128033 · Cited by 181
“Furthermore, these traditional approaches, which involve mathematical morphology and texture analysis, are proving less effective for handling multiscale roads [13]–[16].”
BackgroundFrom W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques
Hou, B., Liu, Q., Wang, H., & Wang, Y. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2948659 · Cited by 181
“Deep learning techniques have gained wide success in many vision tasks in recent years[27], [28], [29].”
BackgroundSelf-citationJiao, J., Zhou, J., Li, X., Xia, M., Huang, Y., Huang, L., Wang, N., Zhang, X., Zhou, S., Wang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103202 · Cited by 119
“In the segmentation of Neck brachial plexus structure and Breast tumor, Unet, ResUnet, Seg-Former, and UperNet under-segment or over-segment due to their limited ability to resolve blurred edges.”
MethodInfluentialSelf-citationQi, K., Yang, H., Li, C., Liu, Z., Wang, M., Liu, Q., & Wang, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_28 · Cited by 159
“ResUNet [16] 0.”
Self-citationHe, H., Yang, D., Wang, S., Wang, S., & Li, Y. (2019) · Remote Sensing · doi:10.3390/rs11091015 · Cited by 174
“As a classical Encoder-Decoder model, the U-net is widely used in medical and remote sensing imagery segmentation [15,20,22,30].”
MethodGhorbanzadeh, O., Crivellari, A., Ghamisi, P., Shahabi, H., & Blaschke, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-94190-9 · Cited by 169
Bangaru, S. S., Wang, C., Zhou, X., & Hassan, M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104602 · Cited by 150
Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
He, S., Bastani, F., Jagwani, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Elshrif, M., Madden, S., & Sadeghi, M. (2020) · European Conference on Computer Vision · doi:10.1007/978-3-030-58586-0_4 · Cited by 149
Müller, D., & Kramer, F. (2019) · BMC Medical Imaging · doi:10.1186/s12880-020-00543-7 · Cited by 149
Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model
Wei, S., Zhang, H., Wang, C., Wang, Y., & Xu, L. (2019) · Remote Sensing · doi:10.3390/rs11010068 · Cited by 148
Self-citationLesion-Aware Dynamic Kernel for Polyp Segmentation
Zhang, R., Lai, P., Wan, X., Fan, D., Gao, F., Wu, X.-J., & Li, G.-B. (2023) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-031-16437-8_10 · Cited by 146
Dense Dilated Network With Probability Regularized Walk for Vessel Detection
Mou, L., Chen, L., Cheng, J. T., Gu, Z., Zhao, Y., & Liu, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2950051 · Cited by 164
“[65], we replace the original blocks in the UNet with ResNet [27] blocks to extract feature in our method.”
MethodLandslide Recognition by Deep Convolutional Neural Network and Change Detection
Shi, W., Zhang, M., Ke, H., Fang, X., Zhan, Z., & Chen, S. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3015826 · Cited by 186
“Keeping this in mind and inspired by [42], we proposed an improved CNN with unpooling layers, as shown in Fig.”
MethodA review of remote sensing image segmentation by deep learning methods
Li, J., Cai, Y., Li, Q., Kou, M., & Zhang, T. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2328827 · Cited by 151
MSCA-Net: Multi-scale contextual attention network for skin lesion segmentation
Sun, Y., Dai, D.-W., Zhang, Q.-N., Wang, Y.-Q., Xu, S.-Y., & Lian, C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109524 · Cited by 142
Self-citationFully transformer network for skin lesion analysis
He, X., Tan, E.-L., Bi, H., Zhang, X., Zhao, S., & Lei, B. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102357 · Cited by 143
Kuras, A., Brell, M., Rizzi, J., & Burud, I. (2021) · Remote Sensing · doi:10.3390/rs13173393 · Cited by 159
“Niemeyer et al. [283] applied an RF classification framework with conditional random fields on lidar data to discriminate vegetation and buildings from each other. vegetation and artificial (roads) and natural ground (grass), respectively.”
BackgroundA Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
Shamsolmoali, P., Zareapoor, M., Wang, R., Zhou, H., & Yang, J. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2925841 · Cited by 159
“Somewhat surprisingly, our model even has better computation efficiency compared to DeepUNet [5] and RU-Net [37].”
MethodResultRoad Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review
Lian, R., Wang, W., Mustafa, N., & Huang, L. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3023549 · Cited by 182
“On the basis of the different outputs of these methods, they can be divided into road segmentation and road centerline extraction [15].”
BackgroundMethodScatterNet: A convolutional neural network for cone‐beam CT intensity correction
Hansen, D., Landry, G., Kamp, F., Li, M.-L., Belka, C., Parodi, K., & Kurz, C. (2018) · Medical Physics (Lancaster) · doi:10.1002/mp.13175 · Cited by 135
“Compared with Zhang et al.30, we do not employ batch-normalization, as it would introduce an unwanted scaling of the scatter.”
MethodInfluentialPixel-wise regression using U-Net and its application on pansharpening
Yao, W., Zeng, Z., Lian, C., & Tang, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.103 · Cited by 133
“, 2018) and remote sensing image segmentation (Zhang et al., 2018).”
BackgroundZhou, Y., Huang, J., Wang, C., Song, L., & Yang, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01928 · Cited by 145
“We compare XNet with supervised state-of-the-arts, including UNet [42], UNet++ [67], Att-UNet [38], HRNet-W18 [47], Res-UNet [63], U 2 -Net [40], UNet 3+ [21], SwinUnet [5] and nnUNet [23].”
MethodLi, M., Long, J., Stein, A., & Wang, X.-Q. (2023) · Isprs Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.04.019 · Cited by 131
“…it with four existing methods for agricultural parcel delineation, corresponding to two single-task networks R2UNet (Zhang et al. 2021), ResUNet (Taravat et al. 2021; Zhang et al. 2018), and two multi-task networks, i. e., ResUNet-a (Waldner and Diakogiannis 2020) and BsiNet (Long et al. 2022).”
BackgroundMethod
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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