A robust road segmentation method based on graph cut with learnable neighboring link weights

Yuan, Jun; Tang, Shuming; Wang, Fei; Zhang, Hong

doi:10.1109/itsc.2014.6957929

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…They got better performance than other methods on 25 images. Yuan et al [ 10 ] presented a novel graph cut method and obtained higher results than other methods. Although these methods alleviate the traditional data-based problems to a certain extent, they cannot achieve better results for images with multiple colors on the road [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

C-UNet: Complement UNet for Remote Sensing Road Extraction

Hou

Liu

Zhang

et al. 2021

Sensors

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Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.

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Section: Introductionmentioning

confidence: 99%

C-UNet: Complement UNet for Remote Sensing Road Extraction

Hou

Liu

Zhang

et al. 2021

Sensors

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“…The most popular region-based methods first segment images into regional objects via typical segmentation algorithms such as graph cut [20], energy functional analysis [21], the watershed algorithm [22], or a support vector machine (SVM)-based method [23,24]. For segmented objects, Shi et al [7,25] and Lei et al [26] used shape features to judge the segmented regions of road or nonroad objects.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

Chen

Zhu

Xie

et al. 2018

IJGI

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Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

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