2019

• Record 97 of
  
  Title:Experimental Studies on Improved Vector Extrapolation Richardson-Lucy Algorithm Used to Realize Wave-front Coded Imaging
  
  Author(s):Zhao, Hui(1); Xia, Jing-Jing(1,3); Zhang, Ling(1,2); Fan, Xue-Wu(1)
  Source: Guangzi Xuebao/Acta Photonica Sinica  Volume: 48  Issue: 6  DOI: 10.3788/gzxb20194806.0611003  Published: June 1, 2019  
  Abstract:An improved vector extrapolation based on Richardson-Lucy algorithm was designed by embedding the modified exponent into the vector extrapolation. The structural similarity index was used as a criterion to determine the optimum iterations and optimum combinations of two acceleration factors. Experimental results show that total iterations are reduced approximately 78.9% and visually satisfactory restoration results can be obtained without denoising the restored image further. This work provides a reference for the development of the Richardson-Lucy algorithm in the application of real-time wave-front coded imaging. © 2019, Science Press. All right reserved.
  Accession Number: 20193107254809
• Record 98 of
  
  Title:Saliency weighted RX hyperspectral imagery anomaly detection
  
  Author(s):Liu, Jiacheng(1,2); Wang, Shuang(1); Liu, Weihua(1); Hu, Bingliang(1)
  Source: Yaogan Xuebao/Journal of Remote Sensing  Volume: 23  Issue: 3  DOI: 10.11834/jrs.20197074  Published: May 25, 2019  
  Abstract:With the development of spectral imaging technique and its data processing technology, anomaly detection using hyperspectral data has become a popular topic. Anomaly detection refers to the search for sparse pixels of unknown spectral signals in hyperspectral imagery. Given that the anomaly detection is unsupervised, providing a priori information is necessary. Thus, anomaly detection has a strong practicality. Considering the lack of spatial correlation and low normal distribution adaptation, the traditional RX algorithm has an inaccurate background estimation. Thus, this algorithm is unsuitable for detecting hyperspectral data. In this study, a saliency weighted RX algorithm is proposed on the basis of the local neighborhood spectra of an image. When the human eye observes an image, the first object that is viewed is frequently the most significant. The significance of the saliency detection algorithm is to identify this goal. The saliency map is a 2D image of the same size as the original image to represent the significance of the corresponding pixel in the original image. In this algorithm, the image background modeling based on probability density is improved by introducing a saliency analysis method. Afterward, the spectral saliency map is established, and the mean vector and covariance matrix of the RX algorithm are redefined. Saliency weighted RX algorithm provides different weights to optimize the background estimation. Anomaly detection experiments are conducted using synthetic and real hyperspectral data. Synthetic data experimental results show that, for each target, the number of anomalies detected using the saliency weighted RX algorithm is more than that of the traditional algorithms, and the saliency weighted RX algorithm can detect anomalies with abundance below 0.1. By contrast, traditional algorithms cannot detect these anomalies. Moreover, the false alarm pixels of the traditional algorithms are distributed in various positions, whereas the saliency weighted RX algorithm concentrates on an area called a false alarm area. This area can be removed effectively by morphological filtering. Real data experimental results show that the saliency weighted RX algorithm corresponds to the largest AUC value and has the optimal detection results. The traditional RX algorithm assumes that the background model follows a multivariate Gaussian distribution and does not perform well in hyperspectral imagery. The method of saliency analysis in the field of computer vision can be effectively analyzed in the spatial domain. This phenomenon compensates for the shortcomings of the RX algorithm to ignore spatial correlation, thus detecting the anomalies synchronized in the spatial and spectral domains. The saliency weighted RX algorithm uses a saliency analysis method to provide the background and anomaly pixels with a different weight, thereby improving the adaptability of the background model. Through the experiment of synthetic and real data, the saliency weighted algorithm can improve the detection probability while reducing the false alarm rate in comparison with the traditional RX algorithm and has a certain anti-noise ability. © 2019, Science Press. All right reserved.
  Accession Number: 20192507062928
• Record 99 of
  
  Title:Tensor representation based target detection for hyperspectral imagery
  
  Author(s):Zhang, Xiao-Rong(1,2,3); Hu, Bing-Liang(1); Pan, Zhi-Bin(2); Zheng, Xi(4)
  Source: Guangxue Jingmi Gongcheng/Optics and Precision Engineering  Volume: 27  Issue: 2  DOI: 10.3788/OPE.20192702.0488  Published: February 1, 2019  
  Abstract:Target detection for Hyperspectral Images (HSIs) is gaining importance owing to its important military and civilian applications. This study proposed a novel target detection algorithm for HSIs based on tensor representation. The algorithm employed tensor analysis including CP and tensor block decompositions to implement blind source separation on hyperspectral data. First, effective spatial and spectral features of the blocks of local images were extracted. Then, a detection model based on sparse and collaborative representations was established. Experiments were conducted to evaluate the performance of our approach under multiple scenes with complex backgrounds. From the visual representation of the results, it can be concluded that the proposed approach effectively extracts the spatial-spectral features from scenes with strong noise and complex backgrounds. The approach has good ability to suppress the background and the target is salient. In addition, the performance of the approach is evaluated using quantitative metrics such as Receiver Operating Curve (ROC) and area under the ROC curve (AUC). Considering the popular HSI image of San Diego as an example, the approach achieves 90% detection rate with a false alarm rate of 10%, and the AUC is greater than 0.95. Hence, our approach outperforms other popular approaches. © 2019, Science Press. All right reserved.
  Accession Number: 20191906900440
• Record 100 of
  
  Title:Parameter inversion of cantilever beam based on polynomial model
  
  Author(s):Song, Yang(1); Wei, Xing(2); Ye, Jing(1,3)
  Source: Journal of Physics: Conference Series  Volume: 1324  Issue: 1  DOI: 10.1088/1742-6596/1324/1/012051  Published: October 14, 2019  
  Abstract:Inverse problem is a kind of problem that "effects" are used to get the "causes". It has broad application prospects in the field of applied mathematics and physics. The paper makes an inversion analysis based on a cantilever beam via polynomial model. An iterative formula is deduced based on Gauss-Newton method to tackle inherent parameter of cantilever beam. In the process of inversing, direct problem is solved for many times. The polynomial model is constructed and taken as a direct problem solver. The method proposed in this paper can make parameter inversion of cantilever beam with variable Young's modulus. The result shows that the method has good stability. It can give some guidance for engineers to solve other inversion problem in engineering. © 2019 IOP Publishing Ltd. All rights reserved.
  Accession Number: 20194607694764
• Record 101 of
  
  Title:Simulation of detecting piston error between segmented mirrors by Fizaeu interference technique on ZEMAX
  
  Author(s):Wei, Limin(1); Wang, Chenchen(2,3); Duan, Wenrui(4)
  Source: Optik  Volume: 183  Issue:   DOI: 10.1016/j.ijleo.2019.02.097  Published: April 2019  
  Abstract:The main method to improve the resolution of optical system is enlarging the pupil of optical system, and by using several segmented mirrors to get an equivalent large diameter primary mirror is a common way. After the deployment on orbit, there will be deviation between deployment position and the designed position, which is position error. The error determines the imaging quality of the optical system. So the precision of the position of segmented mirror is needed to be analyzed to make sure the error will not destroy the image quality. This paper uses Fizaeu interference technique to detect the piston error between segmented mirrors, and analyses the detect theory of it. Build model in the ZEMAX and simulate the change of stripe's position and brightness information. In the end, we get the same result of MATLAB, which testifies Fizaeu is of feasibility to detect the piston error. © 2019 Elsevier GmbH
  Accession Number: 20191006600515
• Record 102 of
  
  Title:A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification
  
  Author(s):Lu, Xiaoqiang(1); Sun, Hao(1,2); Zheng, Xiangtao(1)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 57  Issue: 10  DOI: 10.1109/TGRS.2019.2917161  Published: October 2019  
  Abstract:Remote sensing scene classification (RSSC) refers to inferring semantic labels based on the content of the remote sensing scenes. Recently, most works take the pretrained convolutional neural network (CNN) as the feature extractor to build a scene representation for RSSC. The activations in different layers of CNN (named intermediate features) contain different spatial and semantic information. Recent works demonstrate that aggregating intermediate features into a scene representation can significantly improve the classification accuracy for RSSC. However, the intermediate features are aggregated by some unsupervised feature encoding methods (e.g., Bag-of-Visual-Words). Little attention has been paid to explore the information of semantic labels for the feature aggregation. In this paper, in order to explore the semantic label information, an end-to-end feature aggregation CNN (FACNN) is proposed to learn a scene representation for RSSC. In FACNN, a supervised convolutional features' encoding module and a progressive aggregation strategy are proposed to leverage the semantic label information to aggregate the intermediate features. The FACNN integrates the feature learning, feature aggregation, and classifier into a unified end-to-end framework for joint training. In FACNN, the scene representation is learned by considering the information of semantic labels, which can result in better performance for RSSC. Extensive experiments on AID, UC-Merged, and WHU-RS19 databases demonstrate that FACNN performs better than several state-of-the-art methods. © 1980-2012 IEEE.
  Accession Number: 20200408087082
• Record 103 of
  
  Title:Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection
  
  Author(s):Zhang, Yuanlin(1); Yuan, Yuan(2); Feng, Yachuang(1); Lu, Xiaoqiang(1)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 57  Issue: 8  DOI: 10.1109/TGRS.2019.2900302  Published: August 2019  
  Abstract:Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two data sets have shown the effectiveness of our method. In addition, a large-scale high-resolution remote sensing object detection data set is established to make up for the current situation that the existing data set is insufficient or too small. The data set is available at https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset. © 1980-2012 IEEE.
  Accession Number: 20193107243616
• Record 104 of
  
  Title:Feature Extraction Based on Linear Embedding and Tensor Manifold for Hyperspectral Image
  
  Author(s):Ma, Shixin(1); Liu, Chuntong(1); Li, Hongcai(1); Zhang, Geng(2); He, Zhenxin(1)
  Source: Guangxue Xuebao/Acta Optica Sinica  Volume: 39  Issue: 4  DOI: 10.3788/AOS201939.0412001  Published: April 10, 2019  
  Abstract:In order to express the spatial structure information of hyperspectral image more effectively and improve the classification accuracy after dimensionality reduction, we propose a hyperspectral feature extraction algorithm based on linear embedding and tensor manifold. Different from other manifold structure expression methods, the proposed algorithm uses the cooperative representation theory to solve the weight matrix for globally linear embedding, which is more beneficial to maintain the global information of high dimensional data and improve the accuracy of manifold structure expression. At the same time, the dimension reduction framework of tensor manifold based on multi-feature description is established, and the obtained explicit mapping has strong reliability and global adaptability. Experimental results show that compared with the principal component analysis, locally linear embedding, Laplacian Eigenmap, linearity preserving projection and other algorithms, the proposed algorithm has better classification performance. © 2019, Chinese Lasers Press. All right reserved.
  Accession Number: 20192006931100
• Record 105 of
  
  Title:The spectral-spatial joint learning for change detection in multispectral imagery
  
  Author(s):Zhang, Wuxia(1,2); Lu, Xiaoqiang(1)
  Source: Remote Sensing  Volume: 11  Issue: 3  DOI: 10.3390/rs11030240  Published: February 1, 2019  
  Abstract:Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods. © 2019 by the authors.
  Accession Number: 20190706505805
• Record 106 of
  
  Title:Experimental Studies on the Noise Properties of the Harmonics from a Passively Mode-Locked Er-Doped Fiber Laser
  
  Author(s):Song, Jiazheng(1,2); Hu, Xiaohong(1); Wang, Hushan(1); Duan, Tao(1); Wang, Yishan(1); Liu, Yuanshan(1); Zhang, Jianguo(1)
  Source: IEEE Photonics Journal  Volume: 11  Issue: 6  DOI: 10.1109/JPHOT.2019.2937324  Published: December 2019  
  Abstract:We experimentally investigate the noise properties of a homemade 586 MHz mode-locked laser (MLL). The variation of the timing jitter versus the harmonic order is measured, which is consistent with the theoretical analyses. The dominant contributions to the timing jitter are detailedly studied by analyzing the phase noises at different harmonic frequencies. For low-order harmonics, the intensity noise and relative-intensity-noise-coupled (RIN-coupled) jitter mainly contribute to the timing jitter, while for high-order harmonics, the amplified spontaneous emission (ASE) noise makes the dominant contribution. Then we find that a higher output ratio has an obvious improvement on reducing the timing jitter and suppressing the phase noise because of the shorter pulse duration and lower net cavity dispersion caused by the higher output ratio. Finally a comparison of the noise performance between the MLL and a commercial signal generator is made, which shows that the optically generated radio-frequency signal (OGRFS) has a lower phase noise at high offset frequencies, however the higher phase noise at low offset frequencies leads to a higher timing jitter than the commercial SG. © 2019 IEEE.
  Accession Number: 20200207984238
• Record 107 of
  
  Title:1.8–2.7 μm emission from As-S-Se chalcogenide glasses containing ZnSe: Cr2+ particles
  
  Author(s):Yang, Anping(1); Qiu, Jiahua(1); Ren, Jing(2); Wang, Rongping(3); Guo, Haitao(4); Wang, Yuwei(1); Ren, He(1); Zhang, Jian(1); Yang, Zhiyong(1)
  Source: Journal of Non-Crystalline Solids  Volume: 508  Issue:   DOI: 10.1016/j.jnoncrysol.2019.01.007  Published: 15 March 2019  
  Abstract:Mid-infrared (MIR) light sources are indispensable in modern photonic society. In this work, the composites of the As-S-Se chalcogenide glasses containing MIR-emitting ZnSe: Cr2+ submicron-particles are fabricated by two methods, melt-quenching and hot-pressing. The MIR refractive index, transmittance and photoluminescence properties are investigated and compared in the composites prepared by the two methods. Benefiting from the wide glass forming region of the As-S-Se system, it is possible, by tuning the glass composition, to find a glass (e.g., As40S57Se3) with the refractive index well matching that of the ZnSe: Cr2+ crystal. The composites prepared by the melt-quenching method have higher MIR transmittance, but the MIR emission can only be observed in the samples prepared by the hot-pressing technique. The corresponding reasons are discussed based on microstructural analyses. The results reported in this article could provide helpful theoretical and experimental information for making novel broadband MIR-emitting sources based on chalcogenide glasses. © 2019 Elsevier B.V.
  Accession Number: 20190506452166
• Record 108 of
  
  Title:Magnetic properties and photoluminescence of thulium-doped calcium aluminosilicate glasses
  
  Author(s):So, Byoungjin(1); She, Jiangbo(1,2,3); Ding, Yicong(1); Miyake, Jinsuke(4); Atsumi, Taisuke(4); Tanaka, Katsuhisa(4); Wondraczek, Lothar(1,5,5)
  Source: Optical Materials Express  Volume: 9  Issue: 11  DOI: 10.1364/OME.9.004348  Published: November 1, 2019  
  Abstract:We report on the optical and magnetic properties of Tm2O3-doped calcium aluminosilicate glasses with dopant concentrations of up to 7 mol%. These materials provide a rare case in which high magnetic susceptibility, low Faraday rotation, Tm3+-related infrared photoluminescence and the ability to produce optical fibers are combined. From emission intensity and decay curves of the 3H4→3F4 and 3F4→3H6 transitions, we find cross-relaxation already for 0.5 mol% of Tm2O3 doping, indicating notable Tm2O3 clustering. This facilitates antiferromagnetic interaction and results in high magnetic susceptibility. Substitution of Al2O3 by Tm2O3 induces a more asymmetric local structural environment around Tm3+ species and enhances the diamagnetic contribution to Faraday rotation as opposed to the other rare-earth ions. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
  Accession Number: 20195107878498