Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction

Lu, Xiaoyan; Zhong, Yanfei; Zheng, Zhuo; Liu, Yanfei; Zhao, Ji; Ma, Ailong; Yang, Jie

doi:10.1109/tgrs.2019.2926397

Cited by 144 publications

(64 citation statements)

References 34 publications

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“…There are a lot of applications using semantic segmentation technique in the remote sensing community, such as land use and land cover (LULC) classification [46,23,47], building extraction [45,24,43,15], road extraction [29,9,2,34,3], vehicle detection [35], etc. The main methodologies follow general semantic segmentation, but for special application scenario (e.g.…”

Section: Semantic Segmentation In Remote Sensing Communitymentioning

confidence: 99%

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Zheng

Zhong

Wang

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

215

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Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene. In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based foreground modeling, to alleviate the above two problems. From perspective of relation, FarSeg enhances the discrimination of foreground features via foreground-correlated contexts associated by learning foreground-scene relation. Meanwhile, from perspective of optimization, a foregroundaware optimization is proposed to focus on foreground examples and hard examples of background during training for a balanced optimization. The experimental results obtained using a large scale dataset suggest that the proposed method is superior to the state-of-the-art general semantic segmentation methods and achieves a better trade-off between speed and accuracy. Code has been made available at: https://github.com/Z-Zheng/FarSeg.

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Section: Semantic Segmentation In Remote Sensing Communitymentioning

confidence: 99%

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Zheng

Zhong

Wang

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

215

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“…The tasks of road surface extraction and road centerline extraction are dependent to a certain extent. The road extraction results play a decisive role in the centerline extraction, while the centerline enhances the typical linear features of the road [29]. Therefore, it is beneficial to introduce the concept of multitask learning towards a simultaneous extraction of the road surface and road centerline.…”

Section: Multitask Learningmentioning

confidence: 99%

“…Zhang et al [42] proposed a learning-based road network extraction framework via a multisupervised generative adversarial network, jointly trained by the spectral and topology features of the road networks. Cascading deep learning framework based on multitask networks has been the mainstream idea to solve road-related tasks [27][28][29], which builds the foundation of our work.…”

Section: Introductionmentioning

confidence: 97%

“…Liu et al [28] proposed RoadNet, a multitask convolutional neural network to predict the road surfaces, edges, and centerlines. Lu et al [29] adopted U-Net as the basic network of multitask learning and improved the robustness of feature extraction by applying multiscale feature integration. Batra et al [30] conducted joint learning on the location and division of roads and further improved the connectivity of roads.…”

Section: Introductionmentioning

confidence: 99%

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MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

et al. 2021

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Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction. Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth. Therefore, the studies on road surface extraction and road centerline extraction under complex scenes are insufficient. Meanwhile, most existing efforts addressed these two tasks separately, without considering the possible joint extraction of road surface and centerline. With the introduction of multitask convolutional neural network models, it is possible to carry out these two tasks simultaneously by facilitating information sharing within a multitask deep learning model. In this study, we first design a challenging dataset using remote sensing images from the GF-2 satellite. The dataset contains complex road scenes with manually annotated images. We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction. We take features extracted from the road as the condition of centerline extraction, and the information transmission and parameter sharing between the two tasks compensate for the potential problem of insufficient road centerline samples. In the network design, we use atrous convolutions and a pyramid scene parsing pooling module (PSP pooling), aiming to expand the network receptive field, integrate multilevel features, and obtain more abundant information. In addition, we use a weighted binary cross-entropy function to alleviate the background imbalance problem. Experimental results show that the proposed algorithm outperforms several comparative methods in the aspects of classification precision and visual interpretation.

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“…Compared with patch-wise classification for object detection, semantic segmentation can better carry out spatial context modeling and avoid redundant computation on overlapping areas between patches. Many applications using semantic segmentation techniques have been proposed in the remote sensing literature, such as building extraction [19][20][21][22], road extraction [23][24][25][26][27], vehicle detection [28], land-use and land-cover (LULC) classification [29][30][31], and so on. The main methodologies in these works follow general semantic segmentation but, for some special application scenarios (e.g., vehicles or buildings), many improved techniques [20,25,27] have been proposed for the application scenario.…”

Section: Introductionmentioning

confidence: 99%

EFN: Field-Based Object Detection for Aerial Images

Liu

Zheng

2020

Remote Sensing

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Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

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Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction

Cited by 144 publications

References 34 publications

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

EFN: Field-Based Object Detection for Aerial Images

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