An Effective Road Centerline Extraction Method From VHR

Lian, Renbao; Zhang, Zhenmin; Zou, Changzhong; Huang, Liqin

doi:10.1109/lgrs.2022.3170307

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The commonly used templates in the design process include regular templates and variable templates. The difference is that the templates in the regular templates can be described by regular graphics such as circles and rectangles [ 4 ]. The shape and size of the variable template depends on the imaging process and the characteristics of the target object.…”

Section: Related Workmentioning

confidence: 99%

A new two-step road extraction method in high resolution remote sensing images

Lu,

Shi,

2024

PLoS ONE

View full text Add to dashboard Cite

High-resolution remote sensing technology is an efficient and low-cost space-to-earth observation strategy, which can carry out simultaneous monitoring of large-scale areas. It has incomparable advantages over ground monitoring solutions. Traditional road extraction methods are mainly based on image processing techniques. These methods usually only use one or a few features of images, which is difficult to fully deal with the real situation of roads. This work proposes a two-steps network for the road extraction. First, we optimize a pix2pix model for image translation to obtain the required map style image. Images output by the optimized model is full of road features and can relief the occlusion issues. It can intuitively reflect information such as the position, shape and size of the road. After that, we propose a new FusionLinkNet model, which has a strong stability in the road information by fusing the DenseNet, ResNet and LinkNet. Experiments show that our accuracy and learning rate have been improved. The MIOU (Mean Intersection Over Union) value of the proposed model in road extraction is over 80% in both DeepGlobe and Massachusetts road dataset. The figures are available from https://github.com/jsit-luwei/training-dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

A new two-step road extraction method in high resolution remote sensing images

Lu,

Shi,

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Remote sensing images (RSIs) now have widespread uses in many computer vision (CV) applications [1], including land cover classifications [2], road extractions [3], semantic segmentation [4], target detection [5], and others. However, due to interferences from haze or cloud, the acquired RSIs often suffer from blurring and haziness, which significantly limits the potential of high-level CV applications.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning

Wei,

Cao,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (DCP) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.

show abstract