[Mixed-Spectral Spatial Information Decomposition Model of Water Hyperspectral Inversion].

The effect of Mixed-hyperspectral in the water is difficult in quantitative remote sensing of water. Studies have shown that the only scalar spectrum information is difficult to solve the problem of complex mixed spectra of water. Besides the spectral information, spatial distribution of information is one of the obvious characteristics of the broad waters pollution, and can be used as a useful complement to the remote sensing information and facilitate water complex spectral unmixing. Taking Chaohu as an example, the paper applies the HJ-1A HSI hyperspectral data and the supplemental surface spectral measurement data to discuss the mixed spectra of lake water by spatial statistics and genetic algorithm theory. By using the spatial variogram of geostatistics to simulate the distribution difference of two adjacent pixels, the space-informational decomposition model of mixed spectral in lake water is established by co-kriging genetic algorithm, which is a improved algorithm applying the spatial variogram function of neighborhood pixel as the constraint of the objective function of the genetic algorithm. Finally, the model inversion results of suspended matter concentration are verified. Compared with the conventional spectral unmixing model, the results show the correlation coefficient of the predicted and measured value of suspended sediment concentration is 0.82, the root mean square error 9.25 mg x L(-1) by mixed spectral space information decomposition model, so the correlation coefficient is increased by 8.9%, the root mean square error reduced by 2.78 mg x L(-1), indicating that the model of suspended matter concentration has a strong predictive ability. Therefore, the effective combination of spatial and spectral information of water, can avoid inversion result distortion due to weak spectral signal of water color parameters, and large amount of calculation of information extraction because of the high spectral band numbers, and also provides an effective way to solve spectral mixture model of complex water and improve the accuracy of model inversion.

[Study on polarization spectral feature of suspended sediment in the water body].

Remote sensing of lake water based on water-leaving radiance is to retrieve the concentrations of suspended sediment, phytoplankton and yellow substance which have great impacts on spectrum to assess the water quality. Howerver, because of the complexity of the lake water compositons and the interference between the different components, it is of great difficulty to get accurate results with the reflectance spectrum method developed recently. In the present paper, the authors firstly discussed the reflectance and polarization spectral feature of suspended sediment water body, found out the relations of the reflectance and the degree of polarization of water-leaving radiance and the concentration of suspended sediment at the sensitive bands. The authors also compared the effectiveness of the retrieval approaches based on reflectance and polarization in laboratory water body and Chaohu water body respectively. The results show that in the lake water body where the constituents are very complex, the polarization information has greater capacity of anti-jamming, therefore it will have great potential applictions in lake water quality remote sensing.

[Inversion of the lake total nitrogen concentration by multiple regression kriging model based on hyperspectral data of HJ-1A].

The content of total nitrogen in the waters is an important index to measure lake water quality, and the technique of remote sensing plays a large role in quantitatively monitoring the dynamic change and timely grasping the status of lake pollution. Taking Chaohu as an example, quantitative inversion models of total nitrogen were established by multivariable regression Kriging under analyzing of an correlation between total nitrogen and chlorophyll-a or suspended solids by HIS hyperspectral remote sensing data of HJ-1A satellite. The result shows that the correlation of 0.76 was discovered between total nitrogen and the multiple combination with band 72, band 79 and band 97, while the correlation could be increased to 0.83 by applying combined model of multiple linear regression and ordinary Kriging. The optimization of the residuals of the conventional regression model can improve the accuracy of the inversion effectively. These results also provide useful exploration for further establishing a common model of quantitative inversion of lake total nitrogen concentration.

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder.

The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson-Kendall-Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters' range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.