Statistical analysis of signal measurement in time-of-flight cameras

Three-dimensional imaging systems have evolved significantly in the last two decades due to increase in demand for tasks in the field of close range photogrammetry. The fast and growing need of 3D imaging devices has given rise to range image technology, especially time-of-flight (TOF) cameras, that provide direct measurement of distance between the camera and the targeted surface. A significant advantage of TOF devices over traditional range data sensors is their capability to provide frame rate range data over a full image array. In phase shift TOF cameras, phase shift sampling of the received signal is used to measure amplitude, phase and the offset (intensity) of the received signal. As a result, the quality of the measurement of these sensors depends heavily on signal-to-noise (SNR) of the incoming signal and the subsequent processing algorithms. A detailed understanding of the statistical distributions of the measurement parameters is crucial for accurate distance measurement analysis especially in low SNR scenarios. In this paper, we provide explicit noise models for the three parameters of amplitude, phase and intensity. The proposed stochastic model helps in investigating the effect of noise on signal and classifying range data reliability in TOF cameras. The model is used for prediction of errors in a TOF camera under various SNR conditions. Experimental verification confirms the validity of the model using real data for range error classification under different noise conditions.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

基于概率神经网络的遥感影像分类方法

在对数据进行归一化处理的基础上,将概率神经网络用于遥感影像分类,并探讨样本区的选择和高斯基函数标准差对分类精度的影响。用西藏波密地区1999年的TM遥感影像进行分类试验,并将分类结果和经典的最大似然法进行比较。结果表明：概率神经网络的总体分类精度和Kappa系数分别为94.5%和0.934,取得了较为理想的识别和分类效果。     

基于等维新息灰色马尔可夫模型的大连城市用水量预测

对城市用水量的科学预测是城市水资源合理开发、利用和管理的基础。在传统灰色预测模型的基础上建立了等维新息灰色预测模型,并利用马尔可夫链预测模型预测出结果的波动范围,形成等维新息灰色马尔可夫预测模型。再以1998~2007年大连城市用水量实测值作为原始数据,构建预测模型,预测其2008~2012年用水量。结果表明:等维新息灰色马尔可夫预测模型预测结果的误差更小,精度更高。该预测结果可以为大连未来城市供水规划提供参考依据。     

基于面向对象分类方法在SPOT影像中的地物信息提取

利用基于面向对象分类方法的eCognition软件,以桂林寨底地区为研究区,对影像中各类地物设置不同的分割参数,即初始分割尺度为30,形状因子为0.1,光谱因子为0.9,紧凑度为0.7,光滑度为0.3,能够比较准确地分割出水体、植被、非植被3类地物.根据建立的类层次结构,继续对植被和非植被2个大类进行细分,结果表明当分...     

鲁西北平原深层地下水动力场时空演化

深层地下水是鲁西北平原区特有的含水系统,具有分布范围广、埋藏深和开采恢复能力差等水文地质特征。近30年来的人工开采使天然流场发生了较大改变,形成多个规模不一、形态各异的超采漏斗。利用多年来区域研究成果和实测资料,在研究区域地下水循环系统的基础上,分析开采初期和开采条件下特征年份深层地下水动力场特征,研究鲁西北平原深层地下水演化程度。研究发现,近30年来,区域水头整体下降,地下水动力场发生了显著变化。除黄河三角洲全咸水区、莘县阳谷、嘉祥凸起周围降幅较小外,其余地区均大于20m,其中德州一武城、滨州-博兴、菏泽城区降幅均在60m以上,德州、滨州漏斗中心降幅大于100m。目前已造成德州、滨州、东营、菏泽、济宁等城区不同程度的地面沉降。     

On the Generalized Theory of Atmospheric Particle Systems

Unification is both necessary and challenging for studying atmospheric particle systems, which are polydisperse systems containing particles of different sizes and shapes. A general framework is proposed to realize the first order generalization. Within this generalized framework, (1) atmospheric particle shapes are unified into self-similar fractals; (2) a self-similar particle is characterized by various power-law relationships; (3) by combining these power-law relationships for a single particle with Shannon’s maximum entropy principle and some concepts in statis-tical mechanics, unified maximum likelihoood number size distributions are of the Weibull form for atmospheric particle systems. Frontier disciplines (e. g., scaling, fractal, chaos and hierarchy) are argued to provide potential “tools” for such unification. Several new topics are raised for future research.     

考虑震级精度和可靠性的地震危险性估计

在研究地震危险性时，地震资料的精度和可靠性在研究的时间段内有很大的差别，本文介绍一种考虑这种差别的地震危险性估计方法，作为应用的一例子，计算了汾渭地震带的地震危险性参数，并对结果的不确定性进行了分析和讨论。     

纹理图像统计模型与纹理图像分割

本文详细介绍了一种新的纹理图像统计模型－Ｇｉｂｂｓ模型，给出了两种常用的Ｇｉｂｂｓ分布。在此基础上作者详细推导出了两种纹理图像的分割算法，并用一幅遥感图像进行了实验，获得了较好的分割结果。