Endmember orthonormal mapping in hyperspectral mixture analysis to address endmember variability
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- 作者：Ali Jafari ; Reza Safabakhsh ; Mohammad Mehdi Ebadzadeh
- 刊名：Earth Science Informatics
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：9
- 期：3
- 页码：291-307
- 全文大小：4,521 KB
- 刊物类别：Earth and Environmental Science
- 刊物主题：Earth sciences
Computer Applications in Geosciences
Geosciences
Simulation and Modeling - 出版者：Springer Berlin Heidelberg
- ISSN：1865-0481
- 卷排序：9
文摘
Spectral unmixing estimates the abundance of each endmember at every pixel of a hyperspectral image. Each material in traditional unmixing algorithms is represented through a constant spectral signature. However, endmember variability always exists due to environmental, atmospheric, and temporal conditions, which leads to poor accuracy of the estimated abundances. This paper proposes a new unmixing algorithm based on a new linear transformation called endmember orthonormal mapping (EOM) to overcome the aforementioned problem. The EOM transformation maps original spectral space to a new EOM space to reduce endmember variability. In the original spectral space, each material is represented by a set of spectra (endmember set) which is extracted using the automated endmember bundles (AEB) method. The EOM transforms each endmember set to a vector in the EOM space so that these vectors are orthonormal. On account of orthonormalized endmembers, the condition number of the mixing matrix in the EOM space reduces. Furthermore, we consider the noise term as an additional virtual endmember set mapped to a vector that is orthogonal to other endmembers. As a result, a promising unmixing accuracy is obtained through applying the least squares abundance estimation in the subspace orthogonal to noise. Experimental results of both synthetic and real hyperspectral images demonstrate that the proposed algorithms provide much enhanced performance compared with the state-of-the-art algorithms.KeywordsSpectral unmixingEndmember variabilityCondition numberMultiple endmember spectral mixture analysis (MESMA)Fisher discriminant null space (FDNS)