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Wang W.-B.,Wuhan University of Science and Technology | Wang W.-B.,State Key Laboratory of Remote Sensing Science | Zhang X.-D.,State Oceanic Administration | Wang X.-L.,Wuhan University of Technology
Wuli Xuebao/Acta Physica Sinica | Year: 2013

In order to improve the denoising quality of the pulsar signal, an empirical mode decomposing method (EMD) of pulsar signal denoising based on mode cell proportion shrinking is proposed. Firstly, the pulsar signal is decomposed into a series of intrinsic mode functions (IMF), and the part between the two adjacent zero-crossing within IMF is defined as a mode cell. Then, the optimal proportional shrinking factor is constructed by treating mode cell as the basic unit of analysis. Finally, the all mode cells within IMF are denoised by proportion shrinking, and the mode cell proportion shrinking denoising model is established. The experimental results show that compared with the two EMD denoising algorithms based on coefficient threshold and mode cell threshold, the proposed method can more effectively remove the pulsar signal noise, with better preserving the useful detail information in the original signal. © 2013 Chinese Physical Society. Source


Liu W.P.,Foshan Polytechnic | Wei F.,State Key Laboratory of Remote Sensing Science
Applied Mechanics and Materials | Year: 2014

Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy. © (2014) Trans Tech Publications, Switzerland. Source


Wang W.-B.,Wuhan University of Science and Technology | Wang W.-B.,State Key Laboratory of Remote Sensing Science | Zhang X.-D.,Hubei Engineering University | Wang X.-L.,Wuhan University of Technology
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2013

In order to solve the problem of nonlinear and nonstationary signal de-noising, a novel de-noising method is proposed by combining the principal component analysis (PCA) and empirical mode decomposition (EMD). The method removes noise of intrinsic mode functions(IMFs) using PCA, after the noisy signal is decomposed by EMD. Firstly, the signal details of the first IMF are extracted by using 3σ criterion, and the noise energy of each level IMF is estimated. Secondly, the PCA is implemented on each IMF, and the part of principle components are selected to reconstruct the IMF according to noise energy of IMFs, then the noise of IMF is removed efficiently. Numerical simulation and real data test were carried out to evaluate the performance of the proposed method. The experimental results showed that the proposed method outperformed the Bayesian wavelet threshold de-noising algorithm and mode cell EMD de-noising algorithm. So it is an effective signal de-noising method. Source


Wang W.-B.,Wuhan University of Science and Technology | Wang W.-B.,State Key Laboratory of Remote Sensing Science | Wang X.-L.,Wuhan University of Technology
Wuli Xuebao/Acta Physica Sinica | Year: 2013

In order to improve the de-noising effect of the pulsar signal, an empirical mode decomposition (EMD) denoising algorithm based on the prediction of noise mode cell is put forward. The core steps of the proposed method is as follows: firstly, the noisy pulsar signal is decomposed into a group intrinsic mode function (IMF) by EMD, and the noise mode cell is predicted according to the IMF coefficients statistics and local minimum mean square error criteria. The selected noise mode cells are set to be zero. Then the IMF which has been processed according to noise mode cell prediction is denoised by optimal mode cell proportion shrinking, for removing the noise and retaining the signal details. The experimental results show that compared with the Sure Shrink wavelet threshold algorithm, Bayes Shrink wavelet threshold algorithm and the EMD mode cell proportion shrinking algorithm, the proposed method performs well in removing the pulsar signal noise and retaining the signal details information. The proposed method can achieve a higher signal-to-noise, the lower root mean square error, error of the peak position, relative error of the peak value and phase error. © 2013 Chinese Physical Society. Source


Huang H.,Beijing Forestry University | Liu Q.,State Key Laboratory of Remote Sensing Science | Qin W.,State Key Laboratory of Remote Sensing Science | Qin W.,NASA
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2010

This paper is the first part of a three-part article series. Simulations of directional brightness temperature over both simple canopies with triangular leaves and the row-planted wheat and corn were used to analyze the thermal emission hot-spot effect on crop canopies. Two models, Cupid and TRGM, were successively used to simulate the thermal hot-spot signatures under conditions which cannot be easily captured in reality. The investigation includes the planting row structure, the leaf area index (LAI), the leaf angle distribution (LAD), the component temperature distribution as well as variations in the microclimate. The results show that there are typically three types of directional emission shapes in the solar principle plane: the bowl, dome and bell shape. Regardless of the shape, the hot spot is significant and can be accurately fitted (R2=0.98 and RMSE=0.04°C) with a function of the phase angle (ξ), the hot-spot amplitude (Δ THS}) and the half width of the hot spot (ξ0), which can be quantified with the half width in the RED band. The planting row structure can reduce the Δ THS by a maximum amount (about 1.2 °C) when compared with an unstructured horizontal canopy. The Δ THS is linearly related to the component temperature differences between sunlit and shadowed parts. The linear equation can be used to predict the component temperature differences from Δ THS. The accuracy is very good for the horizontal canopies with triangular leaves (RMSE < 0.4°C and R2 > 0.99), and acceptable for the virtual wheat and corn canopies (RMSE < 1.8°C} and R2 > 0.81). © 2010 IEEE. Source

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