Real-time constrained discriminant analysis to target detection and classification in hyperspectral imagery

Qian Du, Hsuan Ren

研究成果: 雜誌貢獻期刊論文同行評審

64 引文 斯高帕斯(Scopus)

摘要

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a pre-assigned color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.

原文???core.languages.en_GB???
頁(從 - 到)1-12
頁數12
期刊Pattern Recognition
36
發行號1
DOIs
出版狀態已出版 - 1月 2003

指紋

深入研究「Real-time constrained discriminant analysis to target detection and classification in hyperspectral imagery」主題。共同形成了獨特的指紋。

引用此