CSpace
Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification
Xie, Fuding1,2; Li, Fangfei1; Lei, Cunkuan1; Yang, Jun1; Zhang, Yong3
2019-02-01
Source PublicationAPPLIED SOFT COMPUTING
ISSN1568-4946
Volume75Pages:428-440
AbstractHyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies plentiful information to distinguish various land-cover types. However, these spectral bands ordinarily contain a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band feature selection is indispensable for HSI classification. Based on improved subspace decomposition (ISD) and the artificial bee colony (ABC) algorithm, this paper proposes a band selection technique known as ISD-ABC to address the problem of dimensionality reduction in HSI classification. Subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands and using the visualization result of the HSI spectral curve. The artificial bee colony algorithm is first applied to optimize the combination of selected bands with the guidance of ISD and maximum entropy (ME). Using the selected band subset, support vector machine (SVM) with five-fold cross validation is applied for HSI classification. To evaluate the effectiveness of the proposed method, experiments are conducted on two AVIRIS datasets (Indian Pines and Salinas) and a ROSIS dataset (Pavia University). Three indices, namely, overall accuracy (OA), average accuracy (AA) and kappa coefficient (KC), are used to assess the classification results. The experimental results successfully demonstrate that the proposed method provides good classification accuracy compared with six other state-of-the-art band selection techniques. (C) 2018 Elsevier B.V. All rights reserved.
KeywordHyperspectral image Band selection ABC algorithm Subspace decomposition Dimensionality reduction
DOI10.1016/j.asoc.2018.11.014
Language英语
Funding ProjectNational Natural Science Foundation of China[41771178] ; National Natural Science Foundation of China[61772252]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000454941500031
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32074
Collection中国科学院数学与系统科学研究院
Affiliation1.Liaoning Normal Univ, Coll Urban & Environm, Dalian 116029, Liaoning, Peoples R China
2.Chinese Acad Sci, AMSS, KLMM, Beijing 100080, Peoples R China
3.Liaoning Normal Univ, Coll Comp Sci, Dalian 116081, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Xie, Fuding,Li, Fangfei,Lei, Cunkuan,et al. Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification[J]. APPLIED SOFT COMPUTING,2019,75:428-440.
APA Xie, Fuding,Li, Fangfei,Lei, Cunkuan,Yang, Jun,&Zhang, Yong.(2019).Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification.APPLIED SOFT COMPUTING,75,428-440.
MLA Xie, Fuding,et al."Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification".APPLIED SOFT COMPUTING 75(2019):428-440.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xie, Fuding]'s Articles
[Li, Fangfei]'s Articles
[Lei, Cunkuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xie, Fuding]'s Articles
[Li, Fangfei]'s Articles
[Lei, Cunkuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xie, Fuding]'s Articles
[Li, Fangfei]'s Articles
[Lei, Cunkuan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.