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。     

Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

This paper proposes a new set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine‐learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR). In order to achieve high‐fidelity regression models with reduced model errors and bias, this study constructs extensive experimental databases for reinforced and unreinforced concrete joints by collecting existing beam‐column joint subassemblage tests from multiple sources. Various influential parameters that affect joint shear strength such as material properties, design parameters, and joint configuration are investigated through tests of statistical significance. After performing a set of regression analyses, the comparison of simulation results indicates that MARS approach is the best estimation method. Moreover, the accuracy of analytical predictions of the derived MARS model is compared with that of existing joint shear strength relationships. The comparison results show that the proposed model is more accurate compared to existing relationships. This joint shear strength prediction model can be readily implemented into joint response models for evaluation of earthquake performance and inelastic responses of building frames. Copyright © 2014 John Wiley & Sons, Ltd.     

移动互联网时代防震减灾科普宣传的问题及对策研究

在移动互联网蓬勃发展的信息技术背景下,我国防震减灾科普教育存在若干问题,如忽视受众的"数字化"特征,基于智能终端的宣传材料不足;宣传手段不能满足移动互联网时代信息化要求;宣传内容不够生动等,因此提出防震减灾科普知识泛在化宣传新模式,并根据泛在化宣传模式的特征,设计开发基于iOS系统的泛在化宣传资源,为防震减灾宣传工作提供基于移动设备的数字化资源支持。该研究对移动互联网时代地震科普宣传作了新的探讨与尝试,对于当前及未来全民普及防震减灾科学知识有积极的促进作用。     

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield

Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty; characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold deposits, the classification of the neural network results provides an improved prediction of the distribution of the more important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration.     

面向电网巡检的多旋翼无人机航测系统关键技术研究及应用

针对无人机电力巡检所面临的定位精度低、近距离巡检操控难度大、自动化程度低、人为操控事故风险高等问题,本文提出了利用多旋翼无人机RTK差分定位技术、复杂塔型飞行规则机器学习算法等无人机航测系统技术,研发面向电网巡检的无人机智能操作系统及控制终端,提升电网线路日常管理和应急指挥的规范性、便利性和经济性,为电网管理部门提供一种低成本、易操作、高效率、机动灵活的新型巡检模式。     

翻转课堂在地理信息系统课程教学中的实践与思考

针对教师主导的传统教学模式在地理信息系统课程教学中存在的不足,提出了将翻转课堂这一新的混合学习教学模式引入教学的想法。首先分析了地理信息系统课程实施翻转课堂的必要性和可行性,然后在综合考虑地理信息系统课程特点与中外教育文化差异的基础上进行了课程设计和组织实施。实践结果表明,实施翻转课堂确实能够提高学生学习主动性和教学质量,同时也对教师的专业水平和教学能力提出了更高的要求。     

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.     

Charting the emergence of a ‘knowing system’ for climate change adaptation in Australian regional natural resource management

Climate change increases the complexity and uncertainty of regional natural resource management (NRM), calling into question the appropriateness of linear knowledge-transfer approaches. In this paper we reflect on knowledge practices among a partnership of researchers and NRM planners, under a federal program of NRM investment intended to ‘deliver information’ to regional NRM planners to support planning for climate change. We unpack ‘container’ and ‘conduit’ metaphors of linear, one-way communication invoked by the starting conditions, and explore whether more relational ways of communicating were achieved. A key theme emerged early in the research that NRM planners felt overwhelmed by the sheer volume of information available and discouraged by the irrelevance of much of it to their climate change planning. Our research-practice collaboration unfolded in this context and through ongoing face-to-face and virtual engagement over a period of two years. The collaborative approach featured joint identification of priority activities, co-design of planning approaches, and the iterative co-development of an online ‘information portal’, which acted as a boundary object. We report the emergence of a ‘knowing system’, resulting from these efforts to foster relationships and co-produce boundary objects in a particular geographic context. Our findings highlight the potential benefits of investing in the capacity of researchers and NRM practitioners to engage in collaborative research partnerships premised on the emergence of knowing systems.     

基于深度学习的区域生态安全时空模拟与预测

区域作为人类、自然、社会共同作用和互相影响的复杂系统,对区域进行生态量化建模与模拟仿真,是实现区域可持续发展战略的关键。传统机器学习方法对区域生态系统建模取得了一定的成果,但难以确定学习特征和实现时空模拟。深度学习不需事先确定训练特征,具有优异的特征学习能力,能够提高模型预测精度,因此利用深度学习进行建模具有显著优势。本文使用植被净初级生产力（NPP）、气溶胶光学厚度（AOD）和人口格网数据,充分利用深度学习的优点,采用最优深度神经网络时空模拟,得到了河南省2007-2014年3 km分辨率的生态赤字空间分布图和河南省2015-2020年的生态赤字时间预测结果并进行分析,为区域生态的科学管理和建设供科学依据和参考。