Extraction of unstructured roads from satellite images using binary image segmentation

Ghaziani, M.; Mohamadi, Yaser; Koku, Ahmet Buğra; Konukseven, Erhan İlhan

doi:10.1109/siu.2013.6531337

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Assuming that the road pixels in satellite image are brighter than non-road areas in unstructured terrains makes it possible to obtain the road and paths by binarization the satellite image. In order to make a distinction between the road and non-road pixels in the segmentation process, the value for threshold should be computed precisely [20,21]. Determining the value for the binarization threshold using a greyscale histogram includes the following steps.…”

Section: Road Detectionmentioning

confidence: 99%

Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Ghaziani¹,

Konukseven²,

Koku³

2020

acperpro

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Road detection from the satellite images can be considered as a classification process in which pixels are divided into the road and non-road classes. In this research, an automatic road extraction using an artificial neural network (ANN) based on automatic information extraction from satellite images and self-adjusting of the hidden layer proposed. Parameters of non-urban road networks from satellite images using a histogram-based binary image segmentation technique are also presented. The segmentation method is implemented by determining a global threshold, which is obtained from a statistical analysis of a number of sample satellite images and their ground truths. The thresholding method is based on two major facts: first, the points corresponding to non-asphalt roads are brighter than other areas in non-urban images. Second, it is observed that in an aerial image, the area covered by roads is only a small fraction of total pixels. It is also observed that pixels corresponding to roads are generally populated at the very bright end of the image greyscale histogram. In this method, at first, the possible road pixels are selected by the proposed segmentation method. Then different parameters, including color, gradient, and entropy, are computed for each pixel from the source image. Finally, these features are used for the artificial neural network input. The results show that the accuracy of the proposed road extraction method is around 80%.

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Section: Road Detectionmentioning

confidence: 99%

Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Ghaziani¹,

Konukseven²,

Koku³

2020

acperpro

View full text Add to dashboard Cite

show abstract

“…Researchers usually use spectral information to segment the images first, and then extract roads according to shape features [11], texture features [12], spectral characteristics [13] or pixel footprints [14,15]. Alternatively, threshold segmentation based on the gray value of images is the other common pixel-based method [16][17][18]. Its algorithm is not complicated and is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

et al. 2021

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Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35%-12.8% and 2.41-9.8, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

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“…Lue et al [6] used the spectral difference between roads and others to extract road pixels. Ghaziani, M. et al [7] utilized the segmentation method, which set several thresholds based on statistical road features, to achieve the binary classification of road and non-road. Hao Chen et al [8] proposed the fusion of prior topological the road data with a road skeleton to obtain high-accuracy road extraction.…”

Section: Introductionmentioning

confidence: 99%

Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images

et al. 2020

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Road extraction from optical remote sensing images has drawn much attention in recent decades and has a wide range of applications. Most of the previous studies rarely take into account the unique topological characteristics of the road. It is the most apparent feature of linear structure that describes the variety of connection relationships of the road. However, designing a particular topological feature extraction network usually results in a model that is too heavy and impractical. To address the problems mentioned above, in this paper, we propose a lightweight topological space network for road extraction based on knowledge distillation (TSKD-Road). Specifically, (1) narrow and short roads easily influence topological features extracted directly in optical remote sensing images. Therefore, we propose a denser teacher network for extracting road structures; (2) to enhance the weight of topological features, we propose a topological space loss calculation model with multiple widths and depths; (3) based on the above innovations, a topological space knowledge distillation framework is proposed, which aims to transfer different kinds of knowledge acquired in a heavy net to a lightweight net, while significantly improving the lightweight net’s accuracy. Experiments were conducted on two publicly available benchmark datasets, which show the obvious superiority and effectiveness of our network.

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Extraction of unstructured roads from satellite images using binary image segmentation

Cited by 5 publications

References 20 publications

Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images

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