Automated road network extraction using artificial neural network

Kirthika, A.; Mookambiga, A.

doi:10.1109/icrtit.2011.5972323

Cited by 33 publications

(15 citation statements)

References 6 publications

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“…Several studies have attempted to develop fully automated procedures to extract roads from remotely sensed images with high resolution (Bacher and Mayer, 2005;Kirthika and Mookambiga, 2011;Karaman et al, 2012;Zarrinpanjeh et al, 2013). Given road map limitations such as time-consuming field surveys, accurate and timely road network information is detected using high resolution imagery (Resende et al, 2008;Hu et al, 2007;Valero et al, 2010;Das and Mirnalinee, 2011;Xinpeng et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

Shahi

Shafri

Taherzadeh

et al. 2015

The Egyptian Journal of Remote Sensing and Space Science

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This research develops a spectral index to automatically extract asphalt road networks named road extraction index (REI). This index uses WorldView-2 (WV-2) imagery, which has high spatial resolution and is multispectral. To determine the best bands for WV-2, field spectral data using a field spectroradiometer were collected. These data were then analyzed statistically. The bands were selected through the methodology of stepwise discriminant analysis. The appropriate WV-2 bands were distinguished from one another as per significant wavelengths. The proposed index is based on this classification. By applying REI to WV-2 imagery, we can extract asphalt roads accurately. Results demonstrate that REI is automated, transferable, and efficient in asphalt road extraction from high-resolution satellite imagery. Ó 2014 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

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Section: Introductionmentioning

confidence: 99%

A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

Shahi

Shafri

Taherzadeh

et al. 2015

The Egyptian Journal of Remote Sensing and Space Science

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“…Xin et al [22] employ multikernel SVM (M-SVM) for multi-index learning, a morphological building index, a morphological shadow index, and variation indices based on the wavelet transform to separate the road class from other classes. Kirthika and Mookambiga [23] employ a neural network classifier to combine texture measurements and spectral parameters in the road extraction process. Local methods are very useful, especially when the precision aspect is less restrictive than the execution time aspect.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform

Sghaier

Lepage

2016

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

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Road extraction from very high resolution sensors is a very popular topic in panchromatic and multispectral remote sensing image analysis. Despite the vast number of methods proposed in the literature to deal with this problem, in practice, most are quite limited and do not account for geometric and radiometric variability. Our aim is to propose a novel road extraction approach able to efficiently extract roads and reduce computation time using texture analysis and multiscale reasoning based on the beamlet transform. The proposed methodology consists of two stages: 1) road edge candidate selection and 2) multiscale reasoning with the beamlet transform. In the first step, mathematical morphology is applied to distinguish rectilinear structures, and road edge candidates are identified using the Canny edge detector. In the second phase, multiscale reasoning using the beamlet transform allows local and global information to be combined. Global information is introduced to distinguish main road axes at coarser scales, and local segments in finer scales, which are aggregated to reconstruct the road network. Rules based on the spatial relationships between segments belonging to different levels of resolution are also introduced at this stage. The experiments are performed based on the images acquired from the city of Portau-Prince in Haiti during the earthquake of January 2010. The results demonstrate the accuracy and efficiency of our algorithm.

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“…Some of the research efforts present in the literature are supervised classification methods which utilize the common spectral characteristics of roads as features. For example, the study [8] exploits the textural features of a high resolution satellite image to improve performance in addition to their spectral features to extract road network with a neural network based classifier. Likewise, [9] considers the roads as dark regions in the gray-scale representation and extracts a rough road networks by hard thresholding.…”

Section: Introductionmentioning

confidence: 99%

A new approach to automatic road extraction from satellite images using boosted classifiers

et al. 2012

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In this study, a supervised method for automatic road detection based on spectral indices and structural properties is proposed. The need of generalizing the spectral features for the images captured by different kinds of devices is investigated. Mean-shift segmentation algorithm is employed to partition the input multi-spectral image in addition to kmeans which is used as a complementary method for structural feature generation. Adaboost learning algorithm is utilized with extracted features to distinguish roads from non-road regions in the satellite images. The proposed algorithm is tested on an image database containing both IKONOS and GEOEYE images to verify the achieved generalization. The empirical results show that the proposed road extraction method is promising and capable of finding the majority of the road network.

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Automated road network extraction using artificial neural network

Cited by 33 publications

References 6 publications

A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform

A new approach to automatic road extraction from satellite images using boosted classifiers

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