Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/77863
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Cai, L | - |
dc.creator | Shi, W | - |
dc.creator | Miao, Z | - |
dc.creator | Hao, M | - |
dc.date.accessioned | 2018-08-28T01:35:16Z | - |
dc.date.available | 2018-08-28T01:35:16Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/77863 | - |
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 Cai, L.; Shi, W.; Miao, Z.; Hao, M. Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens. 2018, 10, 2, 303,1-13 is available at https://dx.doi.org/10.3390/rs10020303 | en_US |
dc.subject | Accuracy assessment | en_US |
dc.subject | Distance difference | en_US |
dc.subject | Feature similarity | en_US |
dc.subject | Object-based image analysis | en_US |
dc.title | Accuracy assessment measures for object extraction from remote sensing images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 13 | en_US |
dc.identifier.volume | 10 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.3390/rs10020303 | en_US |
dcterms.abstract | Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, Feb. 2018, v. 10, no. 2, 303, p. 1-13 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2018 | - |
dc.identifier.isi | WOS:000427542100147 | - |
dc.identifier.scopus | 2-s2.0-85042532389 | - |
dc.identifier.eissn | 2072-4292 | en_US |
dc.identifier.artn | 303 | en_US |
dc.identifier.rosgroupid | 2017002411 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201808 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 | |
---|---|---|---|---|
Cai_Accuracy_Remote_Sensing.pdf | 1.69 MB | Adobe PDF | View/Open |
Page views
143
Last Week
1
1
Last month
Citations as of May 5, 2024
Downloads
88
Citations as of May 5, 2024
SCOPUSTM
Citations
46
Citations as of May 9, 2024
WEB OF SCIENCETM
Citations
38
Last Week
0
0
Last month
Citations as of May 9, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.