基于ECoG的运动想象脑-机接口分类方法

脑—机接口BCI(Brain-Computer Interface)技术是近年来国际上的研究热点之一,它通常利用脑电EEG(electroencephalo-gram)来实现无动作的人机交互,运动想象是其中一种重要的BCI实验范式。有关研究表明,脑皮层电位ECoG(electrocorticogram)具有更好的信噪比与频带特性。研究基于ECoG的运动想象BCI系统,针对ECoG信号的特点,改进了信号处理方法,提取数据的公共空间模式CSP(Common Spatial Pattern)特征,并利用支持向量机SVM(Support Vector Machines)进行分类器设计,提高了运动意向的识别正确率。用相应方法处理2005年脑-机接口竞赛中的一组实验数据,正确率达到92%,相比于当时参赛时所用的方法提高了6%。实验还发现,支持向量机在克服"维数灾难"和"过拟合"方面具有更好的鲁棒性。     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

从TM影像中提取城镇建筑覆盖区专题信息的改进方法

选用TM影像作为城镇建筑覆盖区专题信息提取的数据源，在Bayes的最大似然法半自动提取城镇专题信息和建筑指数法自动提取城镇专题信息分类方法的基础上，通过二值逻辑演算，综合两种方法的分类结果，去掉不属于城镇的信息，得到真正的城镇建筑覆盖区专题信息。实验结果表明，改进后的分类方法，兼有自动与半自动的优点，其分类精度大大提高。特别是对于短期变化(如防洪区)较大、面积较大而复杂的研究区来说，这一方法的应用弥补了单一分类方法的不足。     

基于改进2DCNN的高光谱遥感图像处理研究

针对传统遥感图像处理中的时间成本和人工成本高、效率低等问题,以提高遥感高光谱图像分类中的处理速度、精度,降低参数量为目标,提出改进的2DCNN模型En-De-2CP-2DCNN.首先,使用1DCNN、2DCNN与3DCNN在Pavia University HSI数据集上分别进行分类实验,对比分析各自优缺点.其次,在保持较快的处理速度和不增加模型参数量的前提下,选择2DCNN为基础模型,参考SegNet的Encoder-Decoder结构,融入双卷积池化思想进行基础模型改进,同时优化学习策略.结果表明:En-De-2CP-2DCNN模型F1为99.96％,达到3DCNN的同等水平(99.36％),较改进前(97.28％)提高2.68个百分点;处理速度(5 s／epoch)和1DCNN位于同一量级,快于3DCNN(96 s／epoch);参数量(2.01 MB)较改进前降低了1.54 MB,虽高于3DCNN(316 KB),但远低于1DCNN(19.21 MB).En-De-2CP-2DCNN模型在处理速度和参数量方面的改进,有利于进一步实现移动端的轻量化部署.     

基于 GAN 网络的电力设施遥感图像去云算法研究

针对电力设施遥感图像云层遮挡问题,提出了基于生成对抗网络(GAN) 的遥感图像去云算法。以条件生成对抗网络 (cGAN) 为主体结构,在编码器自适应填充卷积,设计了基于 Soft Attention的递归神经网络模块,通过对所有特征节点增加 全局依赖关系来解决网络局部最优问题,通过空间信息转换提取关键信息,提高去云与重建效果。实验结果表明,方法对遥 感图像中云层遮挡去除效果较好,重建图像的结构相似性(SSIM) 与峰值信噪比(PSNR) 分别达到0.983与32.899,分别提高 了23.93%与8.86%,均优于其他改进型GAN 网络。研究的方法不仅为基于遥感图像电力设施识别提供了基础,深度学习 遥感图像处理应用提供了参考。     

基于多元统计技术的铀矿蚀变信息高光谱模型

介绍了一种基于数理统计分析技术的铀矿蚀变信息高光谱建模技术,它利用了高光谱数据细微、丰富的光谱信息,根据岩石和蚀变的光谱特征与物质成分的关系,通过矿物中特定离子或离子基团的诊断性吸收谷的特征参数反演岩矿的物质成分,从而综合识别蚀变信息,将为航空和航天成像光谱遥感的应用开辟一个矿物蚀变信息识别和区分的研究途径。     

Potential for identification of wild night-flying moths by remote infrared microscopy

There are hundreds of thousands of moth species with crucial ecological roles that are often obscured by their nocturnal lifestyles. The pigmentation and appearance of moths are dominated by cryptic diffuse shades of brown. In this study, 82 specimens representing 26 moth species were analysed using infrared polarimetric hyperspectral imaging in the range of 0.95–2.5 µm. Contrary to previous studies, we demonstrate that since infrared light does not resolve the surface roughness, wings appear glossy and specular at longer wavelengths. Such properties provide unique reflectance spectra between species. The reflectance of the majority of our species could be explained by comprehensive models, and a complete parametrization of the spectral, polarimetric and angular optical properties was reduced to just 11 parameters with physical units. These parameters are complementary and, compared with the within-species variation, were significantly distinct between species. Counterintuitively to the aperture-limited resolution criterion, we could deduce microscopic features along the surface from their infrared properties. These features were confirmed by electron microscopy. Finally, we show how our findings could greatly enhance opportunities for remote identification of free-flying moth species, and we hypothesize that such flat specular wing targets could be expected to be sensed over considerable distances.     

大幅面高光谱扫描系统在文物研究中的应用

为使高光谱成像技术能够在大幅面绘画类文物研究和保护中充分发挥面分析的优势,研制了大幅面高光谱扫描系统,同时建立了传统颜料数据库。与以往高光谱成像技术对文物进行的局部分析相比,该系统不仅提高了分析效率,而且可对绘画文物整体进行颜料鉴别和相对浓度分布研究,可以为绘画中复杂调色工艺的研究提供巨大的帮助。目前,已使用该系统对故宫博物院东华门天花彩画、纸本书画《丁观鹏画不二尊者图轴》、绢本书画《沈庆兰画贴落》和唐卡《吉祥天母画佛像》进行分析研究,并将其用于文物隐藏病害、隐藏墨迹、底稿线等信息的提取与增强,颜料成分鉴别和混合颜料工艺研究等方面,取得了良好的效果,对绘画类文物的研究与保护具有重要的意义。     

HUMAN LIMITATIONS AND VEHICLE DESIGN∗

This paper contains a review of research on road vehicle design in relation to human physiological, anthropometric and psychological factors     

基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法

多光谱遥感图像的压缩要利用图像谱内及谱间的相关特性。该文在分析多光谱图像谱内和谱间相关特性的基础上,提出了对多光谱遥感图像进行压缩的分段 DPCM和 SPIHT相结合的混合压缩算法,即首先利用分段 DPCM算法去除谱间冗余,再利用高效的 SPIHT小波压缩算法对预测误差图像进行编码。实验取得了令人满意的效果,证明了该算法的有效性。