Railway Detection: From Filtering to Segmentation Networks

Saux, B. Le; Beaupere, Anne; Boulch, Alexandre; Brossard, J.; Manier, A.; Villemin, Guilhem

doi:10.1109/igarss.2018.8517865

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…In RS domain, semantic segmentation is often referred to in numerous applications, such as change detection [28], landcover classification [29], road extraction [30], and building footprint generation [31], and so on. Since the building is an important object among various terrestrial targets in RS imagery, the task of building footprint generation has been heavily studied in the RS community.…”

Section: B Cnn For Building Footprint Generationmentioning

confidence: 99%

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Shi

Huang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from RS imagery is still a challenging task. In this article, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different data sets: 1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich; 2) ISPRS Benchmark data from the city of Potsdam; 3) Dstl Kaggle data set; and 4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-toend building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state of the art.

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Section: B Cnn For Building Footprint Generationmentioning

confidence: 99%

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Shi

Huang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…Currently, railway track detection [18] has gained widespread attention and become an important module of automated train systems. Generally, current methods of railway track detection fall into three categories: vision-based approaches, LiDAR-based approaches, and fusion-based approaches.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

Tang,

Cao,

Yang

et al. 2024

Remote Sensing

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Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train’s operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting.

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“…In RS domain, semantic segmentation is often referred to in numerous applications, such as change detection [32], landcover classification [33], road extraction [34], and building footprint generation [35] and etc. Since the building is an important object among various terrestrial targets in RS imagery, the task of building footprint generation has been heavily studied in the RS community.…”

Section: B Cnn For Building Footprint Generationmentioning

confidence: 99%

Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)

Li,

Shi,

Huang

et al. 2020

Preprint

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This is the preprint version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Building footprint maps are vital to many remote sensing applications, such as 3D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from remote sensing imagery is still a challenging task. In this work, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different datasets: (1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich;(2) ISPRS benchmark data from the city of Potsdam, (3) Dstl Kaggle dataset; and (4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-to-end building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state-of-the-art.

show abstract

Railway Detection: From Filtering to Segmentation Networks

Cited by 10 publications

References 9 publications

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)

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