Show simple item record

dc.contributor.authorSiddiqui, Fasahat Ullah
dc.contributor.authorTeng, Shyh Wei
dc.contributor.authorAwrangjeb, Mohammad
dc.contributor.authorLu, Guojun
dc.date.accessioned2018-01-22T06:11:34Z
dc.date.available2018-01-22T06:11:34Z
dc.date.issued2016
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s16071110
dc.identifier.urihttp://hdl.handle.net/10072/99618
dc.description.abstractExisting automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto24
dc.relation.ispartofissue7
dc.relation.ispartofjournalSensors
dc.relation.ispartofvolume16
dc.subject.fieldofresearchSignal Processing
dc.subject.fieldofresearchAnalytical Chemistry
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchEnvironmental Science and Management
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchcode090609
dc.subject.fieldofresearchcode0301
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0502
dc.subject.fieldofresearchcode0602
dc.titleA Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2016 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/).
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record