Weakly Supervised Road Segmentation in High-Resolution Remote Sensing Images Using Point Annotations

Lian, Renbao; Huang, Liqin

doi:10.1109/tgrs.2021.3059088

Cited by 19 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the pixel-wise annotations, weak annotations (such as scribbles [4,36,37], bounding boxes [38], points [39,40] and image-level tags [41,42]) were much easier to obtain. Therefore, weakly supervised learning was more popular in segmentation tasks [2,43].…”

Section: Semi-supervised and Weakly Supervised Deep Learning Methodsmentioning

confidence: 99%

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

Chen

Peng

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure. Recently, road network extraction using deep learning has become an effective and popular method. The main shortcoming of the road extraction using deep learning methods lies in the fact that there is a need for a large amount of training datasets. Additionally, the datasets need to be elaborately annotated, which is usually labor-intensive and time-consuming; thus, lots of weak annotations (such as the centerline from OpenStreetMap) have accumulated over the past a few decades. To make full use of the weak annotations, we propose a novel semi-weakly supervised method based on adversarial learning to extract road networks from remote sensing imagery. Our method uses a small set of pixel-wise annotated data and a large amount of weakly annotated data for training. The experimental results show that the proposed approach can achieve a maintained performance compared with the methods that use a large number of full pixel-wise annotations while using less fully annotated data.

show abstract

Section: Semi-supervised and Weakly Supervised Deep Learning Methodsmentioning

confidence: 99%

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

Chen

Peng

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Guided by the CNN model, road center points within blocks were iteratively tracked and connected along the road’s direction, completing the road centerline extraction. Building upon this method, Lian and Huang [ 144 ] further developed a point-based weakly supervised road segmentation method for road surface extraction. Point annotation data were initially utilized to detect road seed points and background points in remote sensing images.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Due to the complex content of RS images, the labor cost of producing high-precision road extraction datasets is high and there is a lack of high-precision datasets. Therefore [28] and [29] used weak supervision to perform road segmentation. Similarly, Li et al [30] designed a framework that can learn under noisy labels.…”

Section: A Semantic-segmentation-based Remote Sensing Road Extractionmentioning

confidence: 99%

RUW-Net: A Dual Codec Network for Road Extraction From Remote Sensing Images

Yang,

Gu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Road information plays an increasingly important role in applications such as map updating, urban planning, and intelligent supervision. However, roads in remote sensing images may be shaded by trees and buildings or interfered with by farmland. These intrinsic image features can cause road extraction results to suffer from breakage and misidentification problems. To address these problems, this paper improves on D-LinkNet and proposes a dual codec structure network, namely RUW-Net. Specifically, we use ReSidual U-blocks instead of ordinary residual blocks to extract more global contextual information during the encoding stage. Moreover, we propose a Decoder-Encoder Combination (DEC) module to build a dual codec structure. The DEC module links the decoder of the first U-block and the encoder of the following U-block to narrow the semantic gap in the encoding and decoding process. The RUW-Net model can extract more multi-scale contextual features and effectively use them to enhance the semantic information of road entities. Therefore, the RUW-Net model can obtain more accurate extraction results. We conducted a series of experiments on public datasets such as DeepGlobe, including comparative, robustness, and ablation experiments. The results show that the proposed model alleviates the road extraction breakage and misidentification problems. Compared with other representative methods, the RUW-Net performs better in terms of completeness and accuracy of road extraction results; overall, its extraction results are also the best. The RUW-Net model provides a new idea for road extraction from remote sensing images.

show abstract

Weakly Supervised Road Segmentation in High-Resolution Remote Sensing Images Using Point Annotations

Cited by 19 publications

References 36 publications

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

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

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

RUW-Net: A Dual Codec Network for Road Extraction From Remote Sensing Images

Contact Info

Product

Resources

About