KMS Of Academy of mathematics and systems sciences, CAS
| 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 Publication | APPLIED SOFT COMPUTING
 |
| ISSN | 1568-4946 |
| Volume | 75Pages:428-440 |
| Abstract | Hyperspectral 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. |
| Keyword | Hyperspectral image Band selection ABC algorithm Subspace decomposition Dimensionality reduction |
| DOI | 10.1016/j.asoc.2018.11.014 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[41771178] ; National Natural Science Foundation of China[61772252] |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications |
| WOS ID | WOS:000454941500031 |
| Publisher | ELSEVIER SCIENCE BV |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/32074 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Li, Fangfei |
| Affiliation | 1.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. |
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