dc.contributor |
Háskóli Íslands |
dc.contributor |
University of Iceland |
dc.contributor.author |
Lv, Zhiyong |
dc.contributor.author |
Shi, Wenzhong |
dc.contributor.author |
Benediktsson, Jon Atli |
dc.contributor.author |
Xiaojuan, Ning |
dc.date.accessioned |
2017-09-08T12:57:13Z |
dc.date.available |
2017-09-08T12:57:13Z |
dc.date.issued |
2016-12-15 |
dc.identifier.citation |
Lv, Z.; Shi, W.; Benediktsson, J.A.; Ning, X. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution. Remote Sens. 2016, 8, 1023. doi:10.3390/rs8121023 |
dc.identifier.issn |
2072-4292 |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/385 |
dc.description.abstract |
Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy. |
dc.description.sponsorship |
This work was supported by the Key
Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and
Geoinformation (2015NGCM) and the project from the China Postdoctoral Science Foundation (2015M572658XB),
and the National Natural Science Foundation of China (61302135). |
dc.format.extent |
1023 |
dc.language.iso |
en |
dc.publisher |
MDPI AG |
dc.relation.ispartofseries |
Remote Sensing;8(12) |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Image filter |
dc.subject |
Spatial resolution |
dc.subject |
Aerial image |
dc.subject |
Multiscale segmentation |
dc.subject |
Land cover classification |
dc.subject |
Litrófsgreining |
dc.subject |
Myndvinnsla |
dc.subject |
Loftmyndir |
dc.subject |
Landmælingar |
dc.title |
Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution |
dc.type |
info:eu-repo/semantics/article |
dcterms.license |
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0). |
dc.description.version |
Peer Reviewed |
dc.identifier.journal |
Remote Sensing |
dc.identifier.doi |
10.3390/rs8121023 |
dc.contributor.department |
Rafmagns- og tölvuverkfræðideild (HÍ) |
dc.contributor.department |
Faculty of Electrical and Computer Engineering (UI) |
dc.contributor.school |
Verkfræði- og náttúruvísindasvið (HÍ) |
dc.contributor.school |
School of Engineering and Natural Sciences (UI) |