Remote Sensing Road Extraction by Road Segmentation Network

Tan, Jiahai; Gao, Ming; Yang, Kai; Duan, Tao

doi:10.3390/app11115050

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The simplified decoder part of GNet integrates multi-scale features. Although it has been demonstrated that multi-scale cascading of low-level features in UNet-like architectures can improve the performance of road extraction [114][115][116], we argue that low-level features are usually noisier, and the skip connections between them may lead to uneven boundaries and disruptions. Hence, we replace the multi-scale cascading operations in UNet-like architectures with an asymmetric encoder-decoder structure in GNet so as to meet the requirement of multi-scale feature fusion.…”

Section: Generator Gnetmentioning

confidence: 72%

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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Section: Generator Gnetmentioning

confidence: 72%

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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“…The experimental findings lend credence to our supposition that the HRU-Net model can proficiently delineate road information from these types of images. When appraised on the metrics of precision, recall, and intersection over union (IoU), the HRU-Net model manifested a superior performance in comparison to other cutting-edge methodologies employed for road extraction from remote sensing imagery, such as U-Net, ResNet, DeepLabV3, ResUnet, and HRNet [36][37][38]. These comparative findings portray the HRU-Net model as a promising candidate for implementing road extraction from high-resolution remote sensing images.…”

Section: Discussionmentioning

confidence: 90%

HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion

Yin,

Ren,

Yan

et al. 2023

Applied Sciences

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Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResUnet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions.

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“…Because the KITTI and Middlebury test images do not provide the distance and size of objects in the images, a dual-camera system is made to capture indoor and outdoor images and measured the actual distance and size of objects in the images to verify the estimated data of the proposed algorithm. The overall accuracy (OA) [39] is used to compare the accuracy of the segmented objects with the ground truths in this paper. The OA is defined as Equation (7), where the TP, TN, FP, and NT are true positive, true negative, false positive, and false negative, respectively.…”

Section: Resultsmentioning

confidence: 99%

Three-Dimensional Object Segmentation and Labeling Algorithm Using Contour and Distance Information

Lo¹,

Chiu²,

Yang³

2022

Applied Sciences

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Object segmentation and object labeling are important techniques in the field of image processing. Because object segmentation techniques developed using two-dimensional images may cause segmentation errors for overlapping objects, this paper proposes a three-dimensional object segmentation and labeling algorithm that combines the segmentation and labeling functions using contour and distance information for static images. The proposed algorithm can segment and label the object without relying on the dynamic information of consecutive images and without obtaining the characteristics of the segmented objects in advance. The algorithm can also effectively segment and label complex overlapping objects and estimate the object’s distance and size according to the labeling contour information. In this paper, a self-made image capture system is developed to capture test images and the actual distance and size of the objects are also measured using measuring tools. The measured data is used as a reference for the estimated data of the proposed algorithm. The experimental results show that the proposed algorithm can effectively segment and label the complex overlapping objects, obtain the estimated distance and size of each object, and satisfy the detection requirements of objects at a long-range in outdoor scenes.

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Remote Sensing Road Extraction by Road Segmentation Network

Cited by 6 publications

References 23 publications

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

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

HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion

Three-Dimensional Object Segmentation and Labeling Algorithm Using Contour and Distance Information

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