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Daqing, China

Zhang L.,Daqing Normal University
Guangdian Gongcheng/Opto-Electronic Engineering | Year: 2015

Sparse representation had achieved very good results in hyperspectral imaging anomaly detections. A local joint sparse index method was employed, which combined local spectral sparse index and local spatial sparse index. The influence of the window design on the detection results was discussed. The algorithm combining the adaptive subspace decomposition and the detection method based on local joint sparse index was proposed to improve the detection effect. With synthetic and real hyperspectral imaging datasets in the simulation experiment, the results show that the algorithms utilizing the new models could improve the effectiveness of the detection results to a certain degree, and different window designs have an impact on the results. © 2015, Editorial Office of Opto-Electronic Engineering. All right reserved. Source

Cheng B.,Daqing Normal University
Guangdian Gongcheng/Opto-Electronic Engineering | Year: 2014

The current anomaly detection algorithms are shortage to solving anomaly detection problem because hyperspectral imagery has a higher order features and complicated background information distribution characteristics. By analyzing the spectral features and the spatial features, and exploiting spectral unmixing and subspace divided methods, based on statistical learning theory, an algorithm of anomaly detection of hyperspectral imagery selectivity band subsets is proposed based on spectral unmixing(UNBS-KRX). At first, hyperspectral imagery reduce the background interference and prominent anomaly target information by using spectral unmixing methods, which extract endmembers spectral, that is great influence on background information distribution of hyperspectral imagery. Then, the algorithm divides the whole bands space to a few subspaces. The size of the subspace is different, and non-Gaussian measurement criterion is used to extract the characteristic bands in each subspace. The bands are rich in anomaly target information. At last, as an anomaly detection operator, the kernel RX completes anomaly target detection. The real hyperspectral data sets are used in the experiments, and the result shows the UNBS-KRX is effective and reasonable, and has an excellent detection performance. Source

Liu Y.,Daqing Normal University
Laser Physics Letters | Year: 2013

In LSO host crystal, Yb3+ occupies two different Lu3+ sites and the character makes the spectra of Yb3+:LSO complicated compared with those of Yb3+:YAG. Due to the difference, the model for Yb3+:YAG can not be used directly for Yb3+:LSO. In this letter, on the basis of the spectral information and the energy level diagram, a theoretical model is developed to treat the laser pulse generated from an active CW injected ring cavity with an Yb3+:LSO crystal. Starting from the rate equation, the equation describing the laser pulse is obtained. As a computable model, it also takes into account the pump absorption saturation and the laser reabsorption. For a typical Yb3+ concentration and crystal length, the experimental parameters are chosen by the calculation. © 2013 Astro Ltd. Source

Sun L.Q.,Daqing Normal University
Advanced Materials Research | Year: 2014

In this study, the surface chemistry of TiO2 and related photochemical reaction process was reported by taking Orange II, an azo dye, as an example. The results revealed that major oxidative species is HO instead of hole, though hole is also an oxidative species in some other conditions. Besides that, this article also analysed the reaction process, which involves adsorption-reaction-desorption, three typical steps. For reactions falling in the category of these three steps, Langmuir-Hinshelwood model fit best, because it deals with the adsorption and chemical reaction, a dual process. © (2014) Trans Tech Publications, Switzerland. Source

Cheng B.-Z.,Daqing Normal University | Zhao C.-H.,Harbin Engineering University
Guangdianzi Jiguang/Journal of Optoelectronics Laser | Year: 2013

The high dimensionality of hyperspectral image increases information, but it also leads to the question of dimensionality curse. There are several problems to be solved in reducing dimension, eliminating redundancy of bands, and suppressing background interferences during hyperspectral anomaly targets detection. Aiming at the problems, this paper proposes a new anomaly target detection algorithm of hyperspectral image based on particle swarm optimization (PSO) clustering. Firstly, the algorithm optimizes the traditional-means clustering by using PSO method, the original hyperspectral image is divided for bands subset class by PSO clustering while the features of hyperspectral image bands aren't changed, and those bands with similar features are clustering; Then, the feature information of all band subsets is extracted by using the principal component analysis, which makes the information of anomaly target with highlight and suppresses background interference; At last, the optimal band subsets are achieved by fourth-order cumulant of principal component in band subsets, and anomaly detection is carried on by the kernel RX. The results show that the proposed algorithm was higher precision and lower false alarm probability. Source

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