随机误差对七参数转换模型的影响分析

针对在七参数坐标转换过程中,控制点本身含有的各种误差会对坐标转换的结果产生影响的问题,该文探讨了随机误差对七参数转换模型的影响:以七参数坐标转换模型为研究对象,模拟不同尺度范围的空间直角坐标,给定七参数数值求解另一组空间直角坐标,然后加入不同幅度的随机误差,利用最小二乘准则求解参数,从数值角度分析随机误差对七参数数值以及坐标转换结果的影响。实验结果显示,大范围的七参数模型解算的稳定性优于小范围的模型解算,以及X、Y、Z方向某一方向上的误差对另两个方向的解算结果影响较小。     

结合变差纹理特征的极化SAR建筑物震害信息提取

快速评估建筑物地震灾害信息对地震应急救援工作有着指导意义,而极化SAR具有全天候、全天时的特点,因此利用极化SAR图像提取震害信息已逐渐成为研究热点。虽然极化SAR具有丰富的极化信息,但其纹理信息不可忽略,尤其是完好的人工建筑物在图像上呈现规则的纹理特征,而倒塌建筑区域纹理分布杂乱,因此结合纹理信息也可以很好地提取建筑物信息。以2010年玉树地区的全极化SAR数据为研究对象,首先,利用Yamaguchi分解的体散射分量PV提取了SAR图像中的建筑物区域以及道路、水系等非建筑物信息,在此基础上,对相干散射矩阵T11分量中倒塌建筑物、完好建筑区域进行变差计算,根据变差曲线确定变程a后,再对建筑物区域采取窗口m*m(m=3*a)进行变差计算得到变差纹理信息,最后利用FCM算法对变差纹理信息分别提取完好建筑物和倒塌建筑物区域,为了对比分析,文章利用Yamaguchi分解的二次散射分量PD提取完好建筑物区域,与震后光学遥感图像对应样本点进行人工验证,得到完好建筑物的提取精度为80.18%,倒塌建筑物的提取精度为84.54%,道路水系的提取精度为77.58%。     

Argo floats revealing bimodality of large-scale mid-depth circulation in the North Atlantic

Analysis of Argo float trajectories at 1 000 m and temperature at 950 m in the North Atlantic between November 2003 and January 2005 demonstrates the existence of two different circulation modes with fast transition between them. Each mode has a pair of cyclonic - anticyclonic gyres. The difference is the location of the cyclonic gyre. The cyclonic gyre stretches from southeast to northwest in the first mode and from the southwest to the northeast in the second mode. The observed modes strongly affect the heat and salt transport in the North Atlantic. In particular, the second mode slows down the westward transport of the warm and saline water from the Mediterranean Sea.     

Wavelet analyses of neural networks based river discharge decomposition

The problem of discharge forecasting using precipitation as input is still very active in Hydrology, and has a plethora of approaches to its solution. But, when the objective is to simulate discharge values without considering the phenomenology behind the processes involved, Artificial Neural Networks, ANN give good results. However, the question of how the black box internally solve this problem remains open. In this research, the classical rainfall-runoff problem is approached considering that the total discharge is a sum of components of the hydrological system, which from the ANN perspective is translated to the sum of three signals related to the fast, middle and slow flow. Thus, the present study has two aims (a) to study the time-frequency representation of discharge by an ANN hydrologic model and (b) to study the capabilities of ANN to additively decompose total river discharge. This study adds knowledge to the open problem of the physical interpretability of black-box models, which remains very limited. The results show that total discharge is adequately simulated in the time frequency domain, although less power spectrum is evident during the rainy seasons in the ANN model, due to fast flow underestimation. The wavelet spectrum of discharge represents well the slow, middle and fast flow components of the system with transit times of 256, 12–64 and 2–12 days, respectively. Interestingly, these transit times are remarkably similar to those of the soil water reservoirs of the studied system, a small headwater catchment in the tropical Andes. This result needs further research because it opens the possibility of determining MMT on a fraction of the cost of isotopic based methods. The cross-power spectrum indicates that the error in the simulated discharge is more related to the misrepresentation of the fast and the middle flow components, despite limitations in the recharge period of the slow flow component. With respect to the representation of individual signals of the slow, middle and fast flows components, the three neurons were uncapable to individually represent such flows. However, the combination of pairs of these signals resemble the dynamics and the spectral content of the aforementioned flows signals. These results show some evidence that signal processing techniques may be used to infer information about the hydrological functioning of a basin.     

Relative variation diagrams for describing patterns of compositional variability

In recognizing that a composition, such as a major oxide or sediment composition, provides information only about the relative, not the absolute, magnitudes of its components, this paper exposes the compositional variation array as the simplest and minimum way of summarizing the pattern of variability within a compositional data set. Such summaries are free of the notorious hazards of the constant-sum constraint and when depicted in relative variation diagrams can often provide substantial insights into the nature of the compositional variability. Concepts and practice are illustrated by reference to a number of real data sets.     

基于输出反馈的分数阶奇异线性多智能体系统领导者-跟随一致性

本文主要开展多智能系统领导者-跟随一致性分析,其中每个智能体的动态性能描述为分数阶奇异线性系统.基于系统的输出信息,设计一个输出反馈的控制协议.通过有效的证明,推导出多智能体系统领导者-跟随一致性的充分条件.采用奇异值分解（SVD）技巧,可将一致性条件进一步转换为易于求解的线性矩阵不等式.当通信拓扑图假设为无向连通图时,一致性条件可以简化为相对简单的多个线性矩阵不等式.最后给出一个实例,演示如何求取反馈增益,通过仿真图可以发现本文结果正确、有效.     

FFT在变形监测中的应用与分析

在变形监测中获取的观测数据可以看作是时间与空间上的一组变形信号,一般该信号都会呈现趋势性,隐藏其中的周期性不易被发现;利用FFT对其拟合后的残差和小波分解后的高频信号进行变换与分析,通过时间序列分析对其分析结果建模得到短期的预测、预报,效果比较理想。     

An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods.     

基于混合像元分解的辽河口滨海湿地分类

滨海湿地高精度的地物分类可以为湿地监测与保护提供数据支持和决策依据。以辽河口湿地为研究对象,以Landsat8 OLI多光谱影像为数据源,结合研究区域实际地物情况,采用像元纯度指数和均值波谱法确定端元光谱,并利用全约束最小二乘混合像元技术和决策树技术制定分类规则,最后将研究区域分为芦苇、翅碱蓬、水稻、滩涂、水体(海水、虾池水、河水等)和人工建筑(包括路面、人工设施、房屋等)六大类。结果表明:该算法分类精度高于90%,结合目视判读与野外实地调查,发现分类结果符合实际地物情况。