SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning

Chen, Hao; Peng, Shuang; Du, Chao; Li, Jun; Wu, Songbing

doi:10.3390/rs14174145

Cited by 23 publications

(9 citation statements)

References 53 publications

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“…By processing and analyzing the multi-spectral and multi-band multi-label remote sensing image data, a wealth of geographic information can be obtained, which has extensive applications in various fields. For example, Chen et al [34] extracted information from mountainous road networks, which is of great significance for road planning, traffic management, and environmental protection in mountainous areas. W. Liu et al [35] conducted research on identifying agricultural land parcels, providing accurate land use information for agricultural management and decision-making.…”

Section: Discussionmentioning

confidence: 99%

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

Cheng,

Li,

Deng

et al. 2024

Applied Sciences

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High-resolution remote sensing imagery comprises spatial structure features of multispectral bands varying in scale, color, and shape. These heterogeneous geographical features introduce grave challenges to the fine segmentation required for classification applications in remote sensing imagery, where direct application of traditional image classification models fails to deliver optimal results. To overcome these challenges, a multispectral, multi-label model, MMDL-Net, has been developed. This model is integrated with the multi-label BigEarthNet dataset, primarily employed for land cover classification research in remote sensing imagery, with each image composed of 13 spectral bands and spatial resolutions of 10 m, 20 m, and 60 m. To effectively utilize the information across these bands, a multispectral stacking module has been introduced to concatenate this spectral information. To proficiently process three distinct large-scale remote sensing image datasets, a multi-label classification module has been incorporated for training and inference. To better learn and represent the intricate features within the images, a twin-number residual structure has been proposed. The results demonstrate that the MMDL-Net model achieves a top accuracy of 83.52% and an F1 score of 77.97%, surpassing other deep learning models and conventional methods, thereby exhibiting exceptional performance in the task of multispectral multi-label classification of remote sensing imagery.

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Section: Discussionmentioning

confidence: 99%

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

Cheng,

Li,

Deng

et al. 2024

Applied Sciences

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“…NIGAN helps to extract better road features and prevent the overfitting problem. To reduce the reliance of deep models on large amounts of pixel-level annotated data, Chen et al propose the semiweakly generative adversarial network (SW-GAN), arguing that the OpenStreetMap (OSM) centerline can be considered as sparsely annotated labels [94]. SW-GAN requires only a small amount of precisely annotated data and a large amount of easily available weakly annotated data.…”

Section: Deep Learning Methods For Road Extraction From Hrsismentioning

confidence: 99%

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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“…A novel approach [ 151 ] combines the strengths of semi-supervised and weakly supervised learning, resulting in a method known as semi-weakly supervised learning. In this context, adversarial training from semi-supervised learning and the utilization of weak labels (such as road centerlines) from weakly supervised learning were leveraged to propose a remote sensing image road extraction model named “SW-GAN”.…”

Section: Road Feature Extraction Based On Semi-supervised (Weak) Deep...mentioning

confidence: 99%

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

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SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning

Cited by 23 publications

References 53 publications

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

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

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

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