Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/829
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abe, B. T. | en_US |
dc.contributor.author | Olugbara, Oludayo O. | en_US |
dc.contributor.author | Marwala, T. | en_US |
dc.date.accessioned | 2013-02-07T13:29:53Z | |
dc.date.available | 2013-02-07T13:29:53Z | |
dc.date.issued | 2012 | - |
dc.identifier.citation | Abe, B.T.; Olugbara, O.O. and Marwala, T. 'Hyperspectral Image Classification using Random Forests and Neural Networks.' Proceedings of the World Congress on Engineering and Computer Science. 1(2012). | en_US |
dc.identifier.uri | http://hdl.handle.net/10321/829 | - |
dc.description.abstract | Spectral unmixing of hyperspectral images are based on the knowledge of a set of unknown endmembers. Unique characteristics of hyperspectral dataset enable different processing problems to be resolved using robust mathematical logic such as image classification. Consequently, pixel purity index is used to find endmembers from Washington DC mall hyperspectral image dataset. The generalized reduced gradient algorithm is used to estimate fractional abundances in the hyperspectral image dataset. The WEKA data mining tool is selected to construct random forests and neural networks classifiers from the set of fractional abundances. The performances of these classifiers are experimentally compared for hyperspectral data land cover classification. Results show that random forests give better classification accuracy when compared to neural networks. The study proffers solution to the problem associated with land cover classification by exploring generalized reduced gradient approach with learning classifiers to improve overall classification accuracy. The classification accuracy comparison of classifiers is important for decision maker to consider tradeoffs in accuracy and complexity of methods. | en_US |
dc.format.extent | 6 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | International Association of Engineers | en_US |
dc.subject | Generalized reduced gradient | en_US |
dc.subject | Classifiers | en_US |
dc.subject | Land cover classification | en_US |
dc.subject | Hyperspectral image | en_US |
dc.subject.lcsh | Classifiers | en_US |
dc.title | Hyperspectral image classification using random forests and neural networks | en_US |
dc.type | Article | en_US |
dc.dut-rims.pubnum | DUT-001847 | en_US |
local.sdg | SDG15 | - |
local.sdg | SDG14 | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
Appears in Collections: | Research Publications (Accounting and Informatics) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Abe_Olugbara_Marwala_2012.pdf | 1.09 MB | Adobe PDF | View/Open | |
Permission_IMECS.pdf | Permission Letter | 55.59 kB | Adobe PDF | View/Open |
Page view(s) 20
1,565
checked on Dec 22, 2024
Download(s) 50
1,032
checked on Dec 22, 2024
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.