![](/images/graphics-bg.png)
Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification
Joint Authors
Tang, Yidong
Huang, Shucai
Xue, Aijun
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification.
However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given.
Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper.
In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary.
The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel.
Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required.
Furthermore, the kernel method is employed to improve the interclass separability.
In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm.
Then the query pixel can be labeled by the characteristics of the coding vectors.
Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification.
It enhances the discrimination and hence improves the performance.
American Psychological Association (APA)
Tang, Yidong& Huang, Shucai& Xue, Aijun. 2016. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
Modern Language Association (MLA)
Tang, Yidong…[et al.]. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
American Medical Association (AMA)
Tang, Yidong& Huang, Shucai& Xue, Aijun. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112030