An Ensemble Wasserstein Generative Adversarial Network Method for Road Extraction From High Resolution Remote Sensing Images in Rural Areas

Yang, Caiqin; Wang, Zhenghong

doi:10.1109/access.2020.3026084

Cited by 27 publications

(13 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These images further highlight the effectiveness of the proposed GAN approach, which is particularly effective in preserving the edges of the roads while maintaining high fidelity with the ground truth labels. Also, we compared the performance of the proposed GAN+MUNet approach with other GAN-based road extraction approaches reported in the literature such as GAN+FCN [32], GAN+SegNet [21], E-WGAN [33], MsGAN [34], and McGAN [35] to test the efficacy of the presented model in road extraction. For comparison purpose, that the statistical measure such as the accuracy, recall, and F1 scores reported in the referenced papers vs. our proposed Prop-GAN approach are shown in Table 4.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…The main contribution of this research lies in proposing a GAN with a modified U-Net generative model to extract roads from high-resolution aerial imagery. Compared to prior GANbased road extraction approaches such as GAN+FCN proposed by [32], GAN+SegNet presented by [21], Ensemble Wasserstein Generative Adversarial Network (E-WGAN) proposed by [33], Multi-supervised Generative Adversarial Network (MsGAN) performed by [34], and Multi-conditional Generative Adversarial Network (McGAN) implemented by [35], we introduce the modified U-Net model (MUNet) for the generative term to create a high-resolution smooth segmentation map, with high spatial consistency and clear segmentation boundaries. The proposed model does not require high computational time and a large training dataset and still improves performance and addresses the aforementioned challenges for road extraction from remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

View full text Add to dashboard Cite

Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing highresolution remote sensing imagery. In this study, we propose a generative adversarial network (GAN)-based deep learning approach for road segmentation from high-resolution aerial imagery. In the generative part of the presented GAN approach, we use a modified UNet model (MUNet) to obtain a high-resolution segmentation map of the road network. In combination with simple pre-processing comprising edge-preserving filtering, the proposed approach offers a significant improvement in road network segmentation compared with prior approaches. In experiments conducted on the Massachusetts road image dataset, the proposed approach achieves 91.54% precision and 92.92% recall, which correspond to a Mathews correlation coefficient (MCC) of 91.13%, a Mean intersection over union (MIOU) of 87.43% and a F1-score of 92.20%. Comparisons demonstrate that the proposed GAN framework outperforms prior CNN-based approaches and is particularly effective in preserving edge information.

show abstract

Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Shi et al [35] developed a cGAN architecture using SegNet [36] (based on the encoder-decoder architecture) as G to segment roads in high-resolution aerial imagery and achieved an F1 score of 0.8831 (3.6% improvement when compared to the F1 score of 0.8472 obtained by SegNet when not trained in an adversarial setting). Yang et al [37] added the Wasserstein distance penalty to a GAN to achieve an IoU score of 0.73 when extracting road geometries from rural areas in China.…”

Section: Related Workmentioning

confidence: 99%

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

Cira

Kada

Callejo

et al. 2022

IJGI

View full text Add to dashboard Cite

The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). On the other hand, unsupervised learning does not require labelled data and can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. In this work, we implement a conditional Generative Adversarial Network to reconstruct road geometries via deep inpainting procedures on a new dataset containing unlabelled road samples from challenging areas present in official cartographic support from Spain. The goal is to improve the initial road representations obtained with semantic segmentation models via generative learning. The performance of the model was evaluated on unseen data by conducting a metrical comparison where a maximum Intersection over Union (IoU) score improvement of 1.3% was observed when compared to the initial semantic segmentation result. Next, we evaluated the appropriateness of applying unsupervised generative learning using a qualitative perceptual validation to identify the strengths and weaknesses of the proposed method in very complex scenarios and gain a better intuition of the model’s behaviour when performing large-scale post-processing with generative learning and deep inpainting procedures and observed important improvements in the generated data.

show abstract

“…Recently, GAN-based approaches for postprocessing road extraction emerged. The authors of [39] tackle the road extraction task by adding the Wasserstein distance and gradient penalty to a standard GAN and applying ensembling techniques to achieve an IoU score of 0.73 and obtain road geometries in Chinese rural areas. In [40], a GAN is trained to synthesize arbitrary-sized road network patches and enrich the attributes in areas where the extraction is difficult (where discontinuities are present or in complex areas, such as intersections or highway ramps).…”

Section: Related Workmentioning

confidence: 99%

Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

Cira

Callejo

Alcarria

et al. 2021

Land

View full text Add to dashboard Cite

Remote sensing experts have been actively using deep neural networks to solve extraction tasks in high-resolution aerial imagery by means of supervised semantic segmentation operations. However, the extraction operation is imperfect, due to the complex nature of geospatial objects, limitations of sensing resolution, or occlusions present in the scenes. In this work, we tackle the challenge of postprocessing semantic segmentation predictions of road surface areas obtained with a state-of-the-art segmentation model and present a technique based on generative learning and image-to-image translations concepts to improve these initial segmentation predictions. The proposed model is a conditional Generative Adversarial Network based on Pix2pix, heavily modified for computational efficiency (92.4% decrease in the number of parameters in the generator network and 61.3% decrease in the discriminator network). The model is trained to learn the distribution of the road network present in official cartography, using a novel dataset containing 6784 tiles of 256 × 256 pixels in size, covering representative areas of Spain. Afterwards, we conduct a metrical comparison using the Intersection over Union (IoU) score (measuring the ratio between the overlap and union areas) on a novel testing set containing 1696 tiles (unseen during training) and observe a maximum increase of 11.6% in the IoU score (from 0.6726 to 0.7515). In the end, we conduct a qualitative comparison to visually assess the effectiveness of the technique and observe great improvements with respect to the initial semantic segmentation predictions.

show abstract

An Ensemble Wasserstein Generative Adversarial Network Method for Road Extraction From High Resolution Remote Sensing Images in Rural Areas

Cited by 27 publications

References 36 publications

Improving Road Semantic Segmentation Using Generative Adversarial Network

Improving Road Semantic Segmentation Using Generative Adversarial Network

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

Contact Info

Product

Resources

About