Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/78402
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Gao, LP | - |
dc.creator | Shi, WH | - |
dc.creator | Miao, ZL | - |
dc.creator | Lv, ZY | - |
dc.date.accessioned | 2018-09-28T01:16:26Z | - |
dc.date.available | 2018-09-28T01:16:26Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/78402 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Gao, L., Shi, W., Miao, Z., & Lv, Z. (2018). Method based on edge constraint and fast marching for road centerline extraction from very high-resolution remote sensing images. Remote Sensing, 10(6), 900 is available at https://doi.org/10.3390/rs10060900 | en_US |
dc.subject | Road extraction | en_US |
dc.subject | Very high-resolution image | en_US |
dc.subject | Fast marching method | en_US |
dc.subject | Semiautomatic | en_US |
dc.subject | Edge constraint | en_US |
dc.title | Method based on edge constraint and fast marching for road centerline extraction from very high-resolution remote sensing images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 10 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.3390/rs10060900 | en_US |
dcterms.abstract | In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, June 2018, v. 10, no. 6, 900 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2018 | - |
dc.identifier.isi | WOS:000436561800094 | - |
dc.identifier.eissn | 2072-4292 | en_US |
dc.identifier.artn | 900 | en_US |
dc.description.validate | 201809 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Gao_Edge_Constraint_Fast.pdf | 8.62 MB | Adobe PDF | View/Open |
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