Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques

Chaudhuri, Debasis; Kushwaha, Naveen Kumar; Samal, Ashok

doi:10.1109/jstars.2012.2199085

Cited by 144 publications

(94 citation statements)

References 18 publications

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“…In road extraction from VHR images significant progress has been achieved by the remote sensing and photogrammetry community, e.g., [17,18,20,25,30], and by computer science researchers, e.g., [1,10,12,14,31]. Nevertheless, the ultimate goal of designing and implementing a reliable automatic road extraction system is still far-off [6,12,20,21,29].…”

Section: Proposed Approach and Methodsmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

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Section: Proposed Approach and Methodsmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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“…Particularly, Sensitivity means the number of pixels in the detected road divided by the whole pixels in the road, and specificity denotes one subtracting the number of noise pixels in the detected road divided by the whole pixels in the road. To make performance comparison, other two methods are compared with the proposed method, which are 1) Directional Morphological Enhancement and Segmentation based road detecting method(DMES) [6], and 2) adaptive unsupervised approach for road detecting(AUA) [9]. Firstly, the ROC curves for specificity and sensitivity are illustrated in Fig.4-Fig.9.…”

Section: Methodsmentioning

confidence: 99%

“…This proposed method exploited the properties of road segments to develop customized operators to accurately derive the road segments. The customized operators include directional morphological enhancement, directional segmentation and thinning [6].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, road extraction from remote sensing images is a critical task in image processing. The reason lies in that recognition of roads is a key problem since they form an important GIS layer in significant civilian and military applications including navigation or location aware systems and emergency planning systems for evacuation and fire response [6,7,8,9]. Automatic road extraction algorithm can reduce both time and labor to construct and update the road spatial database in such applications.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic road extraction algorithm can reduce both time and labor to construct and update the road spatial database in such applications. However, fully automated algorithms to recognize them for applications where accuracy is critical are very difficult [6,10,11]. In this paper, we utilize the high resolution remote sensing images, which can obviously enhance the object recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Road Extraction Algorithm for High Resolution Remote Sensing Images

Teng¹,

Shun-lin²,

Zhan³

2014

Appl. Math. Inf. Sci.

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Automatic road extraction from the high resolution remote sensing images is of great importance in intelligent transportation and image processing. Hence, in this paper, an effective road extraction algorithm for high resolution remote sensing images based on the circular projection transformation is proposed. The main idea of the proposed algorithm lies in that the road extraction results are obtained by selecting a suitable initial template, and then search the matched templates through moving the initial template in two directions. Firstly, the circular projection vector of the initial template is achieved by calculating the circular projection value at a specific radius. Secondly, the optimal radius of the circle in circular projection transformation and the length of the seeking step and the seeking angle are determined. Thirdly, for each seeking step the similarity between the target template and the initial template is computed, and the template with the highest similarity is chosen. Finally, roads can be detected by the correct direction by exchanging the first two detected points. To make performance evaluation, the IKONOS dataset is utilized and DMES and AUA algorithm are compared. The experimental results demonstrate that the proposed algorithm can automatic the roads from high resolution remote sensing images effectively and efficiently.

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Investigation of Non-natural Information from Remote Sensing Images: A Case Study Approach

Akhtar

Choubey

Ragavendran

2018

Computational Intelligence and Sustainable Systems

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Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques

Cited by 144 publications

References 18 publications

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

A Novel Road Extraction Algorithm for High Resolution Remote Sensing Images

Investigation of Non-natural Information from Remote Sensing Images: A Case Study Approach

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