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Hyperspectral data subspace dimension algorithm based on noise whitening |
| CHEN Jie1, 2, DU Lei1, LI Jing1, HAN Yachao1, GAO Zihong1 |
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
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Abstract The correlation between adjacent bands of hyperspectral image data is relatively strong. However, signal coexists with noise. The HySime (hyperspectral signal identification by minimum error) algorithm which is based on the principle of least squares is designed to calculate the estimated noise value and the estimated signal correlation matrix value. The algorithm is effective with accurate noise value but ineffective with estimated noise value obtained from spectral dimension reduction and decorrelation process. This paper proposes an improved HySime algorithm based on noise whitening process. Instead of removing noise pixel by pixel, the algorithm carries out the noise whitening process on the original data first, obtains the noise covariance matrix estimated value accurately, and uses the HySime algorithm to calculate the signal correlation matrix value so as to improve the precision of the resultant value. Simulation and experiment have reached some conclusions: Firstly, the improved HySime algorithm is more accurate and stable than the original HySime algorithm; Secondly, the improved HySime algorithm results have better consistency under different conditions compared with the classic NSP (noise subspace the projection) algorithm; Finally, the improved HySime algorithm improves the adaptability of non-white data noise with the noise whitening process.
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| Keywords
fog
inversion
physical parameters
influencing factor
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Issue Date: 03 May 2017
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2017,
Vol. 29
