基于四叉树的LOD地形简化及几何变形纠正

论述了一种基于四叉树结构LOD地形快速简化及几何变形纠正方法。通过建立四叉树结构将地形划分为规则的多个小块,利用一个递归过程遍历四叉树结构。在判断过程中本文将地形粗糙度因素加入到节点评价系统当中,有效地实现了地形的简化。对漫游过程中出现的几何变形现象引入一个混合因子到节点评价系统中,较好地解决了几何变形问题。为了更真实地反映地物地貌,本文将具有较高分辨率的航空影像叠加到DEM地形模型上,取得了较好的效果。     

结合SOM神经网络和混合像元分解的高光谱影像分类方法研究

本文对SOM神经网络算法进行改进,在标类的过程中采用3个策略加以控制,对初始产生的自组织映射图进行调整。通过改进,那些映射到可靠神经元的像素得到了很好的分类,而那些映射到不可靠神经元的像素都被作为不可分像元而提取出来。继而,从混合像元分解的角度来对这些不可分像元进行处理,按类型分解的思想确定混合像元的类别,实现对不可分像元的分类。将SOM神经网络和混合像元分解相结合的分类方法应用于高光谱图像的分类中,通过实验表明了该方法能较好地改善分类效果,提高分类精度。     

Floc size variability under strong turbulence: Observations and artificial neural network modeling

The flocculation of cohesive sediment in the presence of waves is investigated using high-resolution field observations and a newly-developed flocculation model based on artificial neural networks. Vertical profiles of suspended sediment concentration and turbulent intensity are estimated using measurements of current profile and acoustic backscatter. The vertical distribution of floc size is estimated using an artificial neural network (ANN) that is trained and validated using floc size measurements at one vertical level. Data analysis suggests a linear correlation between suspended sediment concentration and turbulence intensity. Observations and numerical simulations show that floc size is inversely related to sediment concentration, turbulence intensity and water temperature. The numerical results indicate that floc growth is supported by low concentration and low turbulence. In the vertical direction, mean size of flocs decreases toward the bottom, suggesting floc breakage due to increasing turbulence intensity toward the bed. A significant decrease in turbulent shear could occur within the bottom few-cm, related to increased damping of turbulence by sediment induced density stratification. The results of the numerical simulations presented here are consistent with the concept of a cohesive sediment particle undergoing aggregation-fragmentation processes, and suggest that the ANN can be a precise tool to study flocculation processes.     

树轮长年表在全球变化相关领域中的应用研究进展-以14C曲线校正、环境考古和地质灾害事件定年为例

近几十年树轮年代学得到了迅速发展,现已成功建立了大量长度逾千年的树轮长年表。本文评述树轮长年表在全球变化相关研究领域中的应用。树轮长年表可为¹⁴C定年提供校准曲线,从而将样本的¹⁴C年龄转化为日历年龄,提高了¹⁴C测年的精度;也可为环境考古如古木建筑及墓葬等提供定年服务,确定古木构建筑的建造年代;树轮长年表在地质灾害事件定年方面也发挥了重要作用。然而,树轮长年表在上述研究领域的应用也存在不足,最新的国际¹⁴C会议公布的¹⁴C校正曲线中能够准确定年的树轮校正部分仅为过去12 410年,目前尚无法提供更长时间尺度的树轮校正曲线。在对木构建筑进行定年时受到古木样本量不足的限制等。因此,为了更好地发挥树轮长年表的应用潜力,应不断发掘与延长现有树轮宽度长年表,同时建立长年表的C、O同位素序列,以期为长年表的应用不断做出贡献。     

Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay,China

Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identification of mangrove forest, and ALI (advanced land imagery) has a large number of infrared bands. Two angle indices were proposed based on liquid water absorption at band 5p and band 5 of EO-1 ALI, denoted as β_1.25 and β_1.65 respectively. A decision tree method was adopted to identify mangrove forest using remote sensing techniques for β_1.25-β_1.65 and NDVI (normalized difference vegetation index) for EO-1 ALI imagery acquired at Shenzhen Bay. The results showed that the reflectance of mangrove forests at band 5p and band 5 was significantly lower than that of terrestrial vegetation due to the characteristics of coastal wetlands of mangrove forests. This resulted in a greater β_1.25-β_1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β_1.25-β_1.65 and NDVI effectively identifies mangrove forest from other land cover categories. The misclassification and leakage rates were 4.29% and 5.11% respectively. ALI sensors with many infrared bands could play an important role in discriminating mangrove forest.     

基于遗传算法优化的BP神经网络在密度界面反演中的应用

BP神经网络方法在二维密度界面的反演中取得了较好的效果,但在反演三维界面时,由于模型更复杂、参数更多,BP神经网络的收敛速度和反演精度都有一定程度的下降。为了改善反演效果,本文利用遗传算法对BP神经网络的权值、阈值选择过程进行优化,获得了更好的网络模型;并将此模型应用于密度界面模型的反演中,预测误差从上百米减小到数十米,同时迭代计算步数减少了近2/3,有效减少了计算时间,反演结果更准确。利用基于遗传算法优化的BP神经网络反演了法国某地区莫霍面深度,预测相对误差仅为1.8%,取得了较好的应用效果。基于遗传算法优化的BP神经网络在密度界面的反演中具有良好的应用价值和研究前景。     

GIS and ANN model for landslide susceptibility mapping

1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a…     

基于植被动态的黄土高原生态地理分区

生态地理区划作为自然区划的新分支,近年来受到国内外地理学者的广泛关注,其在认识地理分异规律及区域规划活动中发挥着重要作用。传统生态地理分区多依据自上而下的三级演绎途径,且对于多分区方案的对比与优选缺乏定量化准则。黄土高原作为中国典型的生态脆弱区,植被生长与恢复对缓解当地生态困境十分重要,因此以植被多年动态一致性特征作为分区合理性的评价指标,有助于准确揭示当地生境特点及分异规律。为此,选取热量类、水分类、地形类及地表覆被类共9个指标,采用自组织映射网络（SOFM）与GIS空间分析技术,基于黄土高原近30年来自然本底与覆被状况进行生态地理分区;并依循植被动态一致性准则,依据两步筛选法对多种方案进行优选,最终将黄土高原分为六大生态地理区。研究表明：黄土高原修正6分区方案在12个备选分区方案中效果最好;同时,修正6分区方案多年平均NPP离散系数最低,表明该分区内部离散程度最小。分区方案与既有分区方案相比具有较好的一致性,但由于区划尺度存在一定的差异,整体区域划分更为清晰。对生态地理分区方案优选定量方法的探索,有助于提升自下而上生态地理区划的客观性。     

城市需水量模拟及不确定性分析方法研究

需水量预测是区域水资源合理配置和有效管理的基础。由于供需水量的不确定性,精准的需水量预测较有困难。通过建立径向基函数（RBF）与BP神经网络预测模型,以西北地区城市西安市需水量为例,将用水量驱动因子作为模型输入,利用1990-2009年20组年用水数据进行网络训练模拟,对2010-2012年3组年用水数据进行检验预测,采用不确定性评价指数和置信区间方法对两种模型及模拟结果进行比较分析。结果表明,RBF与BP模型预测期的均方根误差分别为0.08、0.26,不确定性评价指数分别为0.74、1.02且BP模型预测值的相对误差最大超过10%以上,而RBF模型预测值的相对误差均小于5%,说明RBF模型模拟效果好,具有预测精度高以及不确定性影响低的双重优势;在模拟结果基础上,引用置信区间分析了结果的可靠性,为分析城市需水预测的不确定性提供了依据。     

高分辨率遥感影像目标分类与识别研究进展

高分辨率遥感影像的目标分类与识别,是对地观测系统进行图像分析理解,以及自动目标识别系统提取目标信息的重要手段。本文综述了当前国内外在可见光、红外、合成孔径雷达和合成孔径声纳等遥感影像的目标分类与识别的关键技术和最新研究进展。首先,讨论了高分辨率遥感影像的目标分类与识别问题的主要研究层次和内容;其次,深入分析了高分辨率遥感影像目标分类与识别,在滤波降噪、特征提取、目标检测、场景分类、目标分类和目标识别的关键技术及其所存在的问题;最后,结合并行计算、神经计算和认知计算等技术,讨论了目标分类与识别的可行性方案。具体包括：（1）高性能并行计算在高分辨率遥感图像处理的主流技术,并给出了基于Hadoop+OpenMP+CUDA的高分辨率遥感影像混合并行处理架构;（2）深度学习对于提升目标分类和识别精度的应用前景,以及基于深度神经网络的多层次遥感影像目标识别方法;（3）认知计算在解决遥感影像大数据不确定性分析的模型与算法,并讨论了层次主题模型的多尺度遥感影像场景描述方案。此外,根据媒体神经认知计算的相关研究,探讨了遥感影像大数据的目标分类和识别的发展趋势和研究方向。