森林树种高光谱波段的选择

高光谱是遥感技术发展的一个重要方向，也是地物识别的重要手段。本研究利用地物光谱仪对杉木、雪松、小叶樟树和桂花树4个树种进行高光谱数据测量，探索不同树种在不同波段上的识别能力。研究采用了逐步判别分析法和分层聚类法对实验数据进行数据分析。结果表明：逐步判别分析法选择的波段主要位于红、绿、蓝、和近红外区；分层聚类法选择的波段除了红、绿、蓝、和近红外波段外，还增加了蓝-绿边缘、绿-红边缘和红边区的波段。所选择的波段比原始波段在树种识别时具有更高的精度，最高识别精度达96．77％；边缘区波段对树种的识别有重要作用；用对数-微分变换处理较其他方法处理对树种识别有更好的效果。     

高光谱遥感数据白适应小波滤噪

文章深入分析了高光谱遥感数据中噪声的特点，提出了一种基于平稳小波变换的改进小波滤噪算法。通过对标准图像和PHI高光谱遥感数据实验，证明此方法具有比软阈值方法更好的抑制噪声和保持信号细节的能力，并能良好地拟合高光谱数据中噪声随波长的复杂变化，改善数据处理的效果。     

CMOS image sensors for sensor networks

We report on two generations of CMOS image sensors with digital output fabricated in a 0.6 μm CMOS process. The imagers embed an ALOHA MAC interface for unfettered self-timed pixel read-out targeted to energy-aware sensor network applications. Collision on the output is monitored using contention detector circuits. The image sensors present very high dynamic range and ultra-low power operation. This characteristics allow the sensor to operate in different lighting conditions and for years on the sensor network node power budget. Eugenio Culurciello (S’97–M’99) received the Ph.D. degree in Electrical and Computer Engineering in 2004 from Johns Hopkins University, Baltimore, MD. In July 2004 he joined the department of Electrical Engineering at Yale University, where he is currently an assistant professor. He founded and instrumented the E-Lab laboratory in 2004. His research interest is in analog and mixed-mode integrated circuits for biomedical applications, sensors and networks, biological sensors, Silicon on Insulator design and bio-inspired systems. Andreas G. Andreou received his Ph.D. in electrical engineering and computer science in 1986 from Johns Hopkins University. Between 1986 and 1989 he held post-doctoral fellow and associate research scientist positions in the Electrical and Computer engineering department while also a member of the professional staff at the Johns Hopkins Applied Physics Laboratory. Andreou became an assistant professor of Electrical and Computer engineering in 1989, associate professor in 1993 and professor in 1996. He is also a professor of Computer Science and of the Whitaker Biomedical Engineering Institute and director of the Institute’s Fabrication and Lithography Facility in Clark Hall. He is the co-founder of the Johns Hopkins University Center for Language and Speech Processing. Between 2001 and 2003 he was the founding director of the ABET accredited undergraduate Computer Engineering program. In 1996 and 1997 he was a visiting professor of the computation and neural systems program at the California Institute of Technology. In 1989 and 1991 he was awarded the R.W. Hart Prize for his work on mixed analog/digital integrated circuits for space applications. He is the recipient of the 1995 and 1997 Myril B. Reed Best Paper Award and the 2000 IEEE Circuits and Systems Society, Darlington Best Paper Award. During the summer of 2001 he was a visiting professor in the department of systems engineering and machine intelligence at Tohoku University. In 2006, Prof. Andreou was elected as an IEEE Fellow and a distinguished lecturer of the IEEE EDS society. Andreou’s research interests include sensors, micropower electronics, heterogeneous microsystems, and information processing in biological systems. He is a co-editor of the IEEE Press book: Low-Voltage/Low-Power Integrated Circuits and Systems, 1998 (translated in Japanese) and the Kluwer Academic Publishers book: Adaptive Resonance Theory Microchips, 1998. He is an associate editor of IEEE Transactions on Circuits and Systems I.     

手持热像仪的设计

从手持热像仪的性能要求出发,讨论了手持热像仪设计中应考虑的几个问题,并给出了一个设计实例。     

热成像温差分辨率与微分光谱匹配因数的关系

由近距离低温面目标热成像系统的信号方程和噪声等效温差ＮＥＴＤ的定义，导出了ＮＥＴＤｏｃ１／Ｍ，并由此得到ＭＲＴＤｏｃ１／Ｍ，ＭＤＴＤｏｃ１／Ｍ，从而证明了热成像系统的温差分辨率；ＮＥＴＤ、ＭＲＴＤ、ＭＤＴＤ均要求微发光谱匹配因数Ｍ要大，这为选用光谱区配因数Ｍ和Ｍ作为热成像系统的综合评价参数奠定了理论基础。     

Application of binary optical element to infrared hyperspectral detection

In 1988 Swanson and Veldkamp et al.[1] rectified single lens longitudinal chromatic aberra-tion and spherical aberration based on the characteristics of color-dispersion of BOE, and they prepared a new type of BOE—— a multi-phase structure lens. From then on, a lot of work has been done to explore the application BOE in the optical imaging field[2—5]. However, the charac-teristics of BOE in color-dispersion only depend on incident wavelength, which poses a great problem to investigator…     

基于MNF和SVM的高光谱遥感影像分类研究

通过分析高光谱遥感影像分类的现状及遇到的困难,以OMIS1高光谱数据为实验数据,提出了基于最小噪声分离(Minimum Noise Fraction-MNF)变换和支持向量机(Support Vector Machine-SVM)的高光谱遥感影像分类方法。分类实验结果表明:与传统的最大似然分类法(Maxi mum Likelihood Classification-MLC)比较,该方法克服了Hughes现象,分类速度得以提高,总体分类精度达到94.85%,从而表明了该方法用于高光谱遥感影像分类的实用性和优越性。     

一种基于线性邻域传播的加权K近邻算法

为提高高光谱遥感影像在训练样本不足时的分类精度,提出一种基于线性邻域传播的改进加权K近邻算法.采用线性邻域传播(LNP)算法获取无标签数据属于各类别的概率,将其作为类别信息,以增加训练样本数量,提高K近邻算法的分类效果,并降低错误分类带来的风险.实验结果表明,对于高光谱遥感影像,该算法具有较好的分类效果,优于传统的KNN算法、距离加权KNN算法以及LNP等半监督分类算法.     

Compressed sampling reconstruction of hyperspectral images based on spectral prediction

With increasing amounts of hyperspectral images (HSI) and the limitations of the memory requirements, compressive techniques for hyperspectral images have attracted extensive research efforts in recent years. The main difficulty of applying compressed sampling (CS) theory to compression and reconstruction of hyperspectral images lies in using the spatial correlation and spectral correlation of hyperspectral images. In this paper, a reconstruction algorithm of hyperspectral images taking advantage of two‐dimensional compressed sampling (2DCS) and two‐dimensional total variation (2DTV) incorporating spectral prediction (SP) is investigated. In the sampling process, the hyperspectral images are divided into reference bands and common bands, and all bands are sampled using 2DCS independently. In the reconstruction process, the reference bands are reconstructed by 2DTV first. In order to improve the reconstruction quality of common bands, spectral prediction utilizing the spectral correlation between reference bands and common bands is conducted. Then the spectral compensation is computed by using a combination of the prediction value and the initial approximation for the common bands. The residual between the compensation value and the original value is obtained to revise the approximation for the common bands. The algorithm is implemented in an iterative manner to enhance the performance. Experimental results on popular hyperspectral datasets reveal that the proposed algorithm exploiting spectral prediction outperforms the algorithm 2DCS‐2DTV, which does not use spectral correlation, as well as the state‐of‐the‐art algorithms in terms of peak signal‐to‐noise ratio (PSNR). In particular, when the sampling rate of the reference bands is higher than that of the common bands, the proposed algorithm would improve the reconstruction quality dramatically. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

分子Faraday旋光红外滤波成像技术研究

分子Faraday旋光红外滤波成像器件是一种具有梳状离散透射谱的新型滤波器件。该器件透射光谱由分子能级跃迁决定,因而同时具备高光谱分辨能力及高光学稳定特性。通过顺磁性分子的Faraday旋光效应研究,建立分子Faraday滤波器件的理论模型,并利用量子级联激光光谱技术对滤波器件进行了谱型测试;探讨该技术在红外成像探测系统中的应用,进行基于Faraday旋光红外滤波成像技术的燃烧诊断初步试验,获得了燃烧环境中不受H₂O红外辐射影响的纯NO图像。试验结果表明, Faraday旋光红外滤波成像技术在红外成像遥感探测,尤其是燃烧系统微量成分探测中具有较强的实用性和明显的优越性。