Application Of Empirical Mode Decomposition In Hyperspectral Remote Sensing Data

Hyperspectral remote sensing has high spectral resolution,which can provide almost continuous spectral curve for the pixel.Hyperspectral data is a complex nonlinear and non-stationary signal,EMD is a new adaptive time-frequency analysis method,the characteristics of each mode function after the EMD decomposition can highlight the local characteristics of the original signal,has great advantages,the nonlinear signal analysis and the application of EMD in hyperspectral data.The main work of this paper is:(1)explore common time-frequency analysis methods and their limitations,innovative concept,characteristic mode function study empirical mode decomposition and the principle of the proposed Huang,the decomposition process of EMD algorithm are discussed in detail,and the superior performance of the algorithm itself has the research direction is pointed out the existence of EMD decomposition process the problems and puts forward the.(2)due to the influence of various factors,a large amount of noise is produced in the process of acquiring hyperspectral data.Based on the characteristics of random noise,combined with the characteristics of hyperspectral data,this paper proposes a EMD denoising method based on autocorrelation function.The simulation results show that the signal with noise is decomposed by EMD,mainly concentrated in the high frequency noise component in the IMF,the corresponding noise IMF component after filtering,reconstruction and residual IMF component,noise and signal were effectively separated,in order to achieve high spectral data denoising,by comparison,this method better than wavelet denoising.At the same time,the denoising method is applied to the field measured hyperspectral data.(3)because of the large amount of hyperspectral data and the variability of the core hyperspectral signal,it is not suitable to use the traditional SAM,SCA and other methods to identify the characteristics of hyperspectral data.EMD decomposition of the signal,so that the signal analysis of the real time frequency localization.After the decomposition of EMD,each intrinsic mode function highlights the local characteristics of the original signal.In this paper,we try to apply EMD to the feature extraction of hyperspectral data,so as to find a method which can be used to identify the approximate spectrum,and to ensure that the method is effective and feasible.Taking chalcopyrite and pyrite as examples,the local features are obtained after decomposition,and the recognition of chalcopyrite and pyrite is realized by comparative analysis of the modal function.