多波束声速剖面的动态选取方法

在顾及水体声速变化规律与空间位置及时间相关性的前提下,以时间最近法与距离最近法为基础,利用平均声速模型获得深度误差比,再利用深度误差比分析时间最近法与距离最近法的优劣,从而动态选取声速剖面,同时针对动态选取方法的不足进行扩展。实验结果表明,在削弱声速误差影响方面,当参照的水体声速变化规律与实际情况相符时,扩展后的动态选取方法要优于常用方法。     

一种结合重心法和GIS技术的机场抢修预置物资仓库选址模型

如何科学、合理地进行预置物资仓库的选址是保证机场抢修时效性的关键问题.本文在分析总结传统重心选址法存在的问题的基础上,结合GIS技术,建立了基于道路距离的改进重心法最优选址模型,提高了机场抢修预置物资仓库选址的效率和实用性.     

多特征全极化合成孔径雷达的林地和居民地提取

针对传统全极化合成孔径雷达(SAR)图像信息提取方法存在的问题,结合林地、居民地的散射机理,文章提出了一种综合多特征(多种极化特征、几何形状和尺度特征)的全极化SAR林地和居民地信息提取方法.该方法采用分形网络演化算法实现综合多特征的多尺度分割;基于对象选择极化特征并制定分类规则来提取林地和居民地.实验结果表明,该方法能有效提取研究区的林地和居民地,结果明显优于H-α-A-Wishart分类方法.     

Habitat selection in birds feeding on ocean shores: landscape effects are important in the choice of foraging sites by oystercatchers

Food availability is a fundamental determinant of habitat selection in animals, including shorebirds foraging on benthic invertebrates. However, the combination of dynamic habitats, patchy distributions at multiple spatial scales, and highly variable densities over time can make prey less predictable on ocean‐exposed sandy shores. This can, hypothetically, cause a mismatch between prey and consumer distributions in these high‐energy environments. Here we test this prediction by examining the occurrence of actively foraging pied oystercatchers (Haematopus longirostris) in relation to physical habitat attributes and macrobenthic prey assemblages on a 34 km long, high‐energy beach in Eastern Australia. We incorporate two spatial dimensions: (i) adjacent feeding and non‐feeding patches separated by 200 m and (ii) landscape regions with and without foraging birds separated by 2–17 km. There was no support for prey‐based or habitat‐based habitat choice at the smaller dimension, with birds being essentially randomly distributed at the local scale. Conversely, at the broader landscape dimension, the distribution of oystercatchers was driven by the density of their prey, but not by attributes of the physical beach environment. This scale‐dependence suggests that, on open‐coast beaches, landscape effects modulate how mobile predators respond to variations in prey availability.     

基于选权迭代估计与非监督分类的多光谱图像变化检测

针对多光谱图像的变化检测问题,提出了一种基于选权迭代估计(iterative estimation with weight selection,IEWS)与非监督分类(unsupervised classification,UC)的多光谱图像变化检测方法。借鉴IEWS的思想,并以类似于迭代加权多元变化检测(iteratively reweighted multivariate alteration detection,IRMAD)的迭代模式进行回归估计,得到初步的变化检测结果;并通过对初始变化信息的UC处理,以及对不同类别的IEWS,得到最终的变化检测结果。利用该方法对TM图像进行了实验,结果表明:所得到的变化信息在空间位置上同该区域相应时间段内土地利用/覆盖的变化情况具有很好的一致性;同时与多元变化检测及IRMAD方法变化检测的结果相比较,表明该方法对相对较小的变化信息具有更好的变化检测能力。     

基于ENVI／IDL与ArcGIS Engine的GCP选取与实现

针对遥感图像几何校正中GCP选取受人为因素影响较大、分布与数量难以确定等问题,在分析图像局部特征的基础上,结合Voronoi图,基于ENVI／IDL与ArcGISEngine一体化开发技术,研究了一种具有半自动选取和空间分析功能的GCP选取方法。实验通过一幅面积为3278．47km。的遥感图像进行几何校正,结果表明本文研究的GCP选取方法可以将工作效率提高近一倍,总RMS可降低约0．3个像素,在实际生产中具有一定的参考意义。     

Fishery resource utilization of a restored estuarine borrow pit: A beneficial use of dredged material case study

Numerous pits in coastal waters are subject to degraded water quality and benthic habitat conditions, resulting in degraded fish habitat. A pit in Barnegat Bay, New Jersey (USA) was partially filled with dredged sediment to increase flushing, alleviate hypoxia, and enhance benthic assemblages. Restoration objectives were assessed in terms of benthic community parameters and fishery resource occupation. Restoration resulted in increased benthic diversity (bottom samples) and the absence of water column stratification. Fisheries resources occupied the entire water column, unlike pre-restoration conditions where finfish tended to avoid the lower water column. The partial restoration option effectively reproduced an existing borrow pit configuration (Hole #5, control), by decreasing total depth from −11 m to −5.5 m, thereby creating a habitat less susceptible to hypoxic/anoxic conditions, while retaining sufficient vertical relief to maintain associations with juvenile weakfish and other forage fishes. Partially filling pits using dredged material represents a viable restoration alternative.     

A critical examination of seismic response uncertainty analysis in earthquake engineering

The last decade of performance‐based earthquake engineering (PBEE) research has seen a rapidly increasing emphasis placed on the explicit quantification of uncertainties. This paper examines uncertainty consideration in input ground‐motion and numerical seismic response analyses as part of PBEE, with particular attention given to the physical consistency and completeness of uncertainty consideration. It is argued that the use of the commonly adopted incremental dynamic analysis leads to a biased representation of the seismic intensity and that when considering the number of ground motions to be used in seismic response analyses, attention should be given to both reducing parameter estimation uncertainty and also limiting ground‐motion selection bias. Research into uncertainties in system‐specific numerical seismic response analysis models to date has been largely restricted to the consideration of ‘low‐level’ constitutive model parameter uncertainties. However, ‘high‐level’ constitutive model and model methodology uncertainties are likely significant and therefore represent a key research area in the coming years. It is also argued that the common omission of high‐level seismic response analysis modelling uncertainties leads to a fallacy that ground‐motion uncertainty is more significant than numerical modelling uncertainty. The author's opinion of the role of uncertainty analysis in PBEE is also presented. Copyright © 2013 John Wiley & Sons, Ltd.     

应用稀疏非负矩阵分解聚类实现高光谱影像波段的优化选择

针对高光谱影像数据高维性、高度相关性和冗余性等特点,提出应用稀疏非负矩阵分解聚类实现高光谱影像波段的优化选择。通过稀疏非负矩阵分解方法对高光谱影像进行稀疏化表示,同时顾及其可聚类的特性,在保留所选波段物理意义的基础上,得到波段选择后的高光谱影像降维数据。通过该方法对PHI-3高光谱影像进行波段选择的试验分析,应用聚类特征有效性分析波段聚类结果,并采用波段子集的信息量、相关性和可分性3类评价指标来验证方法的效果。最终,从运行效率和分类精度两方面证明了基于无监督聚类的稀疏非负矩阵分解对高光谱影像的波段选择的实用性。     

Genetic feature selection for texture classification

This paper presents a novel approach to feature subset selection using genetic algorithms. This approach has the ability to accommodate multiple criteria such as the accuracy and cost of classification into the process of feature selection and finds the effective feature subset for texture classification. On the basis of the effective feature subset selected, a method is described to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The methodology presented in this paper is illustrated by its application to the problem of trees extraction from aerial images.