中国旅游城市生态安全系统评估与类型划分研究 ——基于节点权重约束网络DEA模型

采用 DPSIR概念模型作为旅游生态安全系统的基本形态,引入网络 DEA模型测度系统运行效率,从“投入-产出”的研究视角探究旅游生态安全系统内部作用机理。采用结构方程模型对网络 DEA模型的结构及权重加以限制,构建具有节点权重约束的网络 DEA评价模型,对主要旅游城市生态安全系统实例进行实证研究。研究结果认为：将生态安全系统分为自然运行阶段、管理反馈阶段2阶段进行效率评价,加权综合得出各旅游城市生态安全系统的综合评价值,以此为依据将样本城市划分为按系统效率的评价结果将其分划为绿色发展型、稳步发展型、高效发展型、双向提升型和管理能力缺失型,针对各类型旅游城生态安全系统特点提出改善其生态安全系统状况的建议。     

利用半参数模型估计确定验潮组网的深度基准面

深度基准面的确定是进行海洋测量的基础,准确确定深度基准面是进行海洋测量的有效前提。提出了一种将CORS技术与验潮组网技术进行结合的新传递深度基准面的方法,实现长、短期验潮站同步验潮实现深度基准面的增强传递;提出了采用半参数模型理论来求取潮差比的计算方法;具体阐述了验潮组网通过间接平差求取短期验潮站的深度基准面的计算方法,减少了验潮测量的人力投入,计算潮差比时考虑了系统误差的影响,计算短期验潮站时考虑了观测误差对深度基准面传递的影响。     

CORS网区域重力场变化与地面稳定性跟踪监测方法

为了实现地面稳定性降低时间与地点、持续作用时间与空间影响分布的全面跟踪监测,该文基于卫星定位连续运行站(CORS)站网观测数据,结合地表水、大气及海平面变化资料,提出了CORS站网时变重力场及负荷形变场精化的已知负荷移去恢复法,建立了基于时变重力场的确定性地面稳定性变化定量辨识准则。以丽水温州地区为例,利用2015-2017年CORS网及有关水文观测数据进行计算分析,根据40起已发生的历史地质灾害(险情)事件对结果进行验证:丽水温州地区的CORS网具备区域重力场变化与地面稳定性跟踪监测能力,具备地质灾害灾变过程追踪与前兆捕获能力,CORS站网的地质灾害前兆提前捕获率可达92.5%。     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

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.     

广西北斗地基增强系统性能测试与精度分析

在完成广西北斗地基增强系统建设的基础上,测试了系统网络RTK实时定位测量的内符合精度、环境可用性、空间可用性、时间可用性与兼容性;并对GPS+BDS+GLONASS、BDS、GPS+GLONASS三种模式下网络RTK的定位结果进行了对比分析。结果表明,广西北斗地基增强系统的各项精度指标满足设计要求,基于GPS+BDS+GLONASS多系统的网络RTK测量具有较高的精度与较好的稳定性和可靠性。     

小波变换和BP神经网络模型在沉降变形监测中的应用研究

变形预测在预报工程险情方面起着关键性的作用,针对施工中需及时、准确地预测变形的问题,本文利用小波变换原理对监测数据进行降噪处理,并采用BP神经网络分析不同训练样本下的预测效果和精度水平。实验结果表明:基于小波消噪后的BP网络模型,以连续的近期观测数据作为训练样本,对下期变形预测精度高,效果好,相对误差很小。因此,小波变换和BP神经网络模型在沉降变形监测工程中能作为预测研究与应用的参考。     

一种基于复合金字塔模型的三维模型数据组织方法

针对异构三维模型数据的统一组织和集成管理问题,本文以目前主流金字塔模型、三维场景瓦片的数据组织方法和地图服务规范等相关技术为基础,提出了一种基于复合金字塔模型的三维模型数据组织方法。该方法首先构建覆盖测区的地理格网模型,以此作为异构三维模型数据集成的空间框架。其次,建立三维模型数据与地理格网单元的映射关系,从而构建复合金字塔模型,实现异构三维模型数据的统一管理。最后,在数据结构上对现有数据格式进行扩展,设计了一种面向网络传输的数据格式储存三维模型数据,完成异构三维模型数据的一体化存储。试验结果表明,该方法能够实现异构三维模型数据统一组织与集成管理。     

陀螺全站仪在隧道测量中的应用研究

随着我国经济和科技的蓬勃发展,隧道工程建设也呈现出规模化和多元化的趋势。本文首先总结了我国公路隧道、铁路隧道和水下隧道的增长状况;然后归纳了隧道测量中平面控制网的布设方法;最后通过分析发现:通过陀螺全站仪加测陀螺边来传递方位基准,可有效提高隧道贯通精度。     

SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.