Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/62450
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Miao, Z | - |
dc.creator | Shi, WZ | - |
dc.date.accessioned | 2016-12-19T09:00:46Z | - |
dc.date.available | 2016-12-19T09:00:46Z | - |
dc.identifier.issn | 1687-725X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/62450 | - |
dc.language.iso | en | en_US |
dc.publisher | Hindawi Publishing Corporation | en_US |
dc.rights | Copyright © 2016 Zelang Miao and Wenzhong Shi. 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. | en_US |
dc.rights | The following article: Miao, Z., & Shi, W. (2016). A new methodology for spectral-spatial classification of hyperspectral images. Journal of Sensors, 2016, is available at https//doi.org/10.1155/2016/1538973 | en_US |
dc.title | A new methodology for spectral-spatial classification of hyperspectral images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1155/2016/1538973 | en_US |
dcterms.abstract | Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained using the decision fusion rule. Validation tests are performed to evaluate the performance of the proposed approach, and the results indicate that the new proposed approach outperforms the state-of-the-art methods. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of sensors, 2016, 1538973 | - |
dcterms.isPartOf | Journal of sensors | - |
dcterms.issued | 2016 | - |
dc.identifier.isi | WOS:000367980200001 | - |
dc.identifier.eissn | 1687-7268 | en_US |
dc.identifier.rosgroupid | 2015002767 | - |
dc.description.ros | 2015-2016 > Academic research: refereed > Publication in refereed journal | - |
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 | |
---|---|---|---|---|
Miao_A_New_Methodology.pdf | 5.31 MB | Adobe PDF | View/Open |
Page views
115
Last Week
1
1
Last month
Citations as of Apr 14, 2024
Downloads
111
Citations as of Apr 14, 2024
SCOPUSTM
Citations
10
Last Week
0
0
Last month
Citations as of Apr 19, 2024
WEB OF SCIENCETM
Citations
6
Last Week
1
1
Last month
Citations as of Apr 18, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.