Road extraction of high-resolution satellite remote sensing images in U-Net network with consideration of connectivity

Wang, Bin; Chen, Z. X.; Wu, Liang; XIE, Peng; Fan, Donglin; Fu, Bolin

doi:10.11834/jrs.20209301

Cited by 3 publications

(2 citation statements)

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“…Deep learning can fully explore the feature information in remote sensing images and has gradually become more popular in water body extraction applications [18][19][20]. Among the deep learning models, U-Net has proven to exhibit a high extraction accuracy and minimal spatial resolution losses [21,22], making it suitable for precise water body extraction. However, current research on deep learning-based water body extraction methods primarily focuses on optical remote sensing, while relatively few studies have been conducted on microwave remote sensing [23].…”

Section: Introductionmentioning

confidence: 99%

Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region

Li,

Xu,

Zhou

et al. 2023

Remote Sensing

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An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies. For this purpose, this study employs Sentinel-1 synthetic aperture radar (SAR) data, complemented by water indices and terrain features, to conduct research in the Poyang Lake area. The results indicate that the proposed multi-source data water extraction method based on microwave remote sensing data can quickly and accurately extract a large range of water bodies and realize long-time monitoring, thus proving a new technical means for the accurate extraction of floodwater bodies in the Poyang Lake region. Moreover, the comparison of several methods reveals that CAU-Net, which utilizes multi-band imagery as the input and incorporates a channel attention mechanism, demonstrated the best extraction performance, achieving an impressive overall accuracy of 98.71%. This represents a 0.12% improvement compared to the original U-Net model. Moreover, compared to the thresholding, decision tree, and random forest methods, CAU-Net exhibited a significant enhancement in extracting flood-induced water bodies, making it more suitable for floodwater extraction in the hilly Poyang Lake region. During this flood monitoring period, the water extent in the Poyang Lake area rapidly expanded and subsequently declined gradually. The peak water area reached 4080 km2 at the height of the disaster. The severely affected areas were primarily concentrated in Yongxiu County, Poyang County, Xinjian District, and Yugan County.

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Section: Introductionmentioning

confidence: 99%

Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region

Li,

Xu,

Zhou

et al. 2023

Remote Sensing

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“…The SGCN proficiently extracts road features and enhances noise interference mitigation throughout the road extraction process. Wang et al [24] used a convolutional neural network to extract roads from high-resolution remote sensing images, which also optimized the road breaks appearing in the extraction results, and finally obtained complete extraction results for roads. Although deep learning methods have made some progress in road extraction, they still face some challenges.…”

Section: Introductionmentioning

confidence: 99%

An Improved U-Net Network for Sandy Road Extraction from Remote Sensing Imagery

Nie,

An,

Chen

et al. 2023

Remote Sensing

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The extraction of sandy roads from remote sensing images is important for field ecological patrols and path planning. Extraction studies on sandy roads face limitations because of various factors (e.g., sandy roads may have poor continuity, may be obscured by external objects, and/or have multi-scale and banding characteristics), in addition to the absence of publicly available datasets. Accordingly, in this study, we propose using the remote sensing imagery of a sandy road (RSISR) dataset and design a sandy road extraction model (Parallel Attention Mechanism-Unet, or PAM-Unet) based on Gaofen-2 (GF-2) satellite images. Firstly, the model uses a residual stacking module, which can solve the problem of poor road feature consistency and improve the extraction of fine features. Secondly, we propose a parallel attention module (PAM), which can reduce the occlusion effect of foreign objects on roads during the extraction process and improve feature map reduction. Finally, with this model, the SASPP (Strip Atrous Spatial Pyramid Pooling) structure, which enhances the model’s ability to perceive contextual information and capture banding features, is introduced at the end of the encoder. For this study, we conducted experiments on road extraction using the RSISR dataset and the DeepGlobe dataset. The final results show the following: (a) On the RSISR dataset, PAM-Unet achieves an IoU value of 0.762, and its F1 and IoU values are improved by 2.7% and 4.1%, respectively, compared to U-Net. In addition, compared to the models Unet++ and DeepLabv3+, PAM-Unet improves IoU metrics by 3.6% and 5.3%, respectively. (b) On the DeepGlobe dataset, the IoU value of PAM-Unet is 0.658; compared with the original U-Net, the F1 and IoU values are improved by 2.5% and 3.1%, respectively. The experimental results show that PAM-Unet has a positive impact by way of improving the continuity of sandy road extraction and reducing the occlusion of irrelevant features, and it is an accurate, reliable, and effective road extraction method.

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