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101.
A voice enhancement algorithm based on the Empirical Mode Decomposition (EMD) and the improved spectral subtraction is proposed for the low-SNR (Signal Noise Ratio) shortwave time signal. This method is proposed to solve the problem that the shortwave time signal cannot be used for timing in complex noisy environments. The core idea of this method is to use the Hilbert-Huang Transform (HHT) algorithm to make the empirical mode decomposition on the noisy shortwave signal, and to select the intrinsic mode functions containing the shortwave signal information for the signal reconstruction by through the maximum correlation. Then, to make the spectral subtraction on the reconstructed signal to achieve the purpose of noise reduction. The experimental result shows that this method has a better noise reduction than the traditional methods.  相似文献   
102.
长江口盐度梯度下不同形态碳的分布、来源与混合行为   总被引:1,自引:0,他引:1  
河口碳的生物地球化学过程是全球碳循环的重要组成。通过测定溶解无机碳(DIC)及其稳定同位素丰度(δ13CDIC),溶解有机碳(DOC),有色溶解有机物(CDOM),颗粒有机碳(POC)及其稳定同位素丰度(δ13CPOC)与元素比值(N/C)及相关指标,研究了2014年7月长江口盐度梯度下不同形态碳的分布、来源和混合行为。结果表明,DIC浓度、DOC浓度、POC含量分别为1 583.2~1 739.6 μmol/L,128.4~369.4 μmol/L和51.2~530.8 μmol/L,这些不同形态碳及CDOM的荧光组分的分布模式相似,均是从口内到口外,整体呈现先增大后减小的趋势,并与盐度呈现非保守混合行为。添加作用主要发生在在口门处最大浑浊带附近。与含量相反,从口内到口外,δ13CDIC和δ13CPOC均呈现逐渐减小再增大的趋势,在口门附近达到最低值,分别为-9.7‰和-26.7‰。在口门附近不同形态碳含量上升及δ13CDIC、δ13CPOC的降低可能主要与沉积物再悬浮及微生物作用有关。基于蒙特卡洛模拟的三端元混合模型的结果显示,河口内外POC来源变化明显,口内POC以陆源有机碳贡献为主,平均为62.3%,口外海源贡献逐渐增加。CDOM相关参数结果表明长江口CDOM主要来自陆源输入,海源及人类活动等也对其产生影响。  相似文献   
103.
基于GPS新型L5信号的地表雪深反演研究   总被引:1,自引:0,他引:1  
利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点,因此其可作为一种代替气象站监测雪深的新手段。然而,先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性,本文阐述了GPS-R技术反演雪深的原理,利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度,通过获取频谱特征值与天线高度的关系求解雪深值,最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明:与现有的GPS-R测量雪深结果相比,利用新型的L5反射信号反演地表雪深的精度更佳;采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。  相似文献   
104.
差分层析SAR是在层析SAR基础上发展起来的一种四维信息反演技术。它不仅实现了对雷达目标的三维分辨能力,同时可以获取目标的形变速率信息,可实现对目标方位-距离-高度-时间四维成像,这对实现城市基础设施动态形变监测、古建筑风险评估、重要工程安全监测等应用具有重要实际意义和价值。本文基于差分层析SAR成像原理,分析了成像处理过程中存在的问题,总结了差分层析SAR成像算法研究现状和特点;最后列举了差分层析SAR技术的主要应用领域,并对其技术发展趋势进行了展望。  相似文献   
105.
机载WIDAS数据的Landsat卫星反照率初步验证   总被引:1,自引:1,他引:0  
随着精细化监测的需求,中高空间分辨率的地表反照率产品逐渐成为气候模型的主要输入。目前,中高空间分辨率反照率产品的验证主要基于地表站点的通量塔观测数据,区域机载飞行数据的验证依然相对较少。因此,本文基于区域机载数据验证Landsat反照率产品。针对内蒙古自治区根河森林试验区所获取的机载红外广角双模式成像仪(WIDAS)多角度反射率数据,应用BRDF原型反演算法估算其反照率,分析了应用机载数据验证中高空间分辨率反照率产品的潜力。2016年内蒙古根河森林试验区机载WIDAS飞行多角度观测的可用多角度范围为25°,以前的研究表明BRDF原型反照率反演算法表现出对小观测角度的反照率反演结果的鲁棒性。因此,机载WIDAS反照率在一定程度可用于星载反照率的验证。首先,基于核驱动模型和各向异性平整指数(AFX)提取了试验区4种MODIS二向性反射分布函数(BRDF)原型;然后,将其作为先验知识应用到根河森林WIDAS机载数据的反照率反演中;最后,用WIDAS反照率和单个地面通量塔观测的反照率对Landsat卫星数据的反照率进行初步验证。验证结果表明Landsat反照率与WIDAS反照率结果较为一致,但略有低估,总体均方根误差(RMSE)约为0.02,偏差为0.0057。在多角度观测范围较小时,BRDF原型的反照率反演算法可为星载地表反照率的验证提供了一种有效的验证手段。  相似文献   
106.
马培培  李静  柳钦火  何彬彬  赵静 《遥感学报》2019,23(6):1232-1252
对多源遥感数据协同生产的2010年—2015年中国区域1 km空间分辨率5天合成的MuSyQ(Multi-source data Synergized Quantitative remote sensing production system)叶面积指数LAI产品进行验证。参考现有的LAI产品(MODIS c5,GLASS LAI)和中国生态系统研究网络部分农田和森林站点可用的LAI地面测量数据,从时空连续性、时空一致性、精度和准确性等方面对中国区域的MuSyQ LAI产品进行定性和定量分析与评价。结果表明:(1) MuSyQ LAI产品在保证精度优于MODIS产品的情况下,时间分辨率和时空连续性均有提高。MuSyQ LAI与其他LAI产品(MODIS c5,GLASS LAI)在整体上有很好的一致性(RMSE=1.0,RMSE=0.81),但对常绿阔叶林高值处的描述不稳定;(2) 与LAI地面测量数据相比,MuSyQ LAI产品与地面参考图对比结果较好(最高相关性(R2=0.54)和较低总体误差(RMSE=0.96)),其在阔叶作物生长季高值处有些许低估且在某些阔叶林站点有些高估。整体上,MuSyQ LAI产品呈现出较高的精度,可靠的空间分布和连续稳定的时间分布,且对森林LAI的描述具有更可靠的动态范围。  相似文献   
107.
徐聪辉  李彩 《遥感学报》2019,23(6):1078-1090
水体体散射函数是描述水体中光在各方向散射特性的一个重要的固有光学参数,对水色遥感、水环境监测与保护、海气交互过程研究、水下军事目标跟踪等领域具有十分重要的意义。水体体散射函数测量尤其是覆盖0°—180°范围的广角体散射函数测量技术起步较晚,直接测量难度较大,一直以来都是一个棘手的科学难题。本文梳理了国内外广角水体体散射函数测量技术的发展历程,对于广角体散射函数测量技术,从原理上大致可以分为单一探测器转动式、多个固定探测器阵列式以及面阵CCD成像式等3大类,对不同测量原理下的测量技术进行详细分析,并对广角水体体散射函数测量技术在海洋生物光学、气泡跟踪、水色遥感等方面的相关应用进展进行总结和归纳,并对未来广角水体体散射函数的发展趋势和应用前景进行展望。  相似文献   
108.
With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.  相似文献   
109.
The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.  相似文献   
110.
In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.  相似文献   
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