高光谱目标探测的进展与前沿问题

针对高光谱目标探测问题的主要挑战,将高光谱目标探测的进展与前沿问题分为两个方面进行综述。基于信号检测理论的方法如结构化背景的约束能量最小化方法、非结构化背景的自适应一致性余弦评估器等,是高光谱目标的探测经典算法;随着统计模式识别与机器学习领域中新技术的出现,一些数据驱动的目标探测方法逐渐成为了高光谱目标探测的前沿问题,如核方法、稀疏表达方法等。概述了两类方法的特点,比较了各自的优势和不足,并展望了高光谱目标探测未来的发展趋势。     

素质教育背景下地理教师的GIS素质构建

在对地理教师地理科学素质现状分析的基础上,对地理教师在教学、科研、生活中应具备的地理信息科学素质进行了研究,并从师范教育、继续教育2个角度探讨了构建地理教师地理信息科学素质的方法。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

遥感影像分析在新增建设用地督察中的应用

以卫星影像大数据和时空数据挖掘技术为基础,采用深度学习框架支撑,构建了面向新增建设用地遥感监测应用需求的深度学习训练平台,可智能发现自然资源督察线索,全面提高自然资源督察问题的发现能力.该技术可广泛服务于大范围、高频次的督察工作,为督察工作提供更及时客观全面的信息基础和保障.     

基于城市竞争力的资源型城市空间布局研究

在总结已有的界定原则基础上,对中国资源型城市进行了界定和划分,确定了51个地级市及以上资源型城市,并从地理空间和城市竞争力的角度分析了这些城市的在空间位置上的分布规律。研究发现,我国资源型城市大多分布在中西部和偏北地区,而城市竞争力排名靠前的城市,无论在资源类型还是地理区位条件上都有着很明显的共性。可见地理区位因素隐藏在资源禀赋背后,对城市发展有着显著的影响。     

基于在线惯序极限学习机的瞬变电磁非线性反演

基于梯度下降法的传统人工神经网络瞬变电磁反演方法计算效率低,不能保证全局收敛。为了解决上述问题,提出一种在线惯序极限学习机(online sequential extreme learning machine, OSELM)的瞬变电磁反演方法。该方法针对瞬变电磁法所获取的高维勘探数据进行建模反演,首先,通过随机设定隐层参数(输入权值和偏差)来简化模型的学习过程;然后,将测试得到的预测样本加入训练样本中,作为下一次的更新信息,建立在线贯序极限学习机预测模型,从而最大限度提高反演精度;最后,设计了两个经典的瞬变电磁层状地电模型并进行了拟二维地电模型的反演。反演结果表明,该方法能够较好地解决瞬变电磁法高维数据非线性建模的反演问题,同时相较极限学习机(extreme learning machine, ELM),非线性反演方法具有更加准确的反演结果、更好的泛化能力以及更高的计算效率,为神经网络在地球物理反演中的应用提供了新思路。     

Industrial and geographical mobility of workers during industry decline: The Swedish and German shipbuilding industries 1970–2000

This article follows the industry employment histories of all individuals who at some point have been affiliated with the declining German or the dismantling Swedish shipbuilding industry during 1970–2000. We analyse the situation of the individual workers leaving shipbuilding, investigating the extent to which they were employed at all, tended to move to related sectors within or outside the region, and whether such moves were beneficial for the individuals. Combining insights from labour geography and redundancy studies with evolutionary economic geography, we find remarkably similar results for the West German and Swedish cases. Our findings indicate a notable impact of the regional industry structure on the labour market outcomes for workers leaving shipbuilding. This suggests that more attention should be devoted to the specific structures of the absorptive capacity of regional labour markets. The findings are discussed within the context of a mature industry.     

Geographical Conditions and National Strategies

This paper is a summary based on the relationship between national conditions and development strategies for decades of consulting research. Through cases, the paper outlined the basic meaning of national conditions and geographical conditions and their close relationship with national development strategies, and emphasized the importance of comprehensive thinking and scientific judgment in the process of carrying out the governments’ strategies and development policies, and making development policies. At last, suggestions were made that the points of China’s national conditions and the rules of connection with government decision-making should be written into geography textbooks as well as into textbooks for party schools of the Communist Party of China.     

The fisherly imagination: The promise of geographical approaches to marine management

The management of the sea has increased exponentially in the last half-century, and different academic disciplines have been vital in shaping this management. Human geography, despite its explicit focus on the human–environment nexus, has so far had little impact on human relations with the sea. Based on empirical research conducted in England and Scotland, we argue that human geography is uniquely placed to offer effective solutions to marine resource management problems, and that geographers have the potential to offer key insights into how human populations can best interact with the living seas. Three of the most important current scholarly ‘imaginations’ of the sea, and the policies they inform (economics and market-based management, conservation biology and area based protection, and anthropology and community management), are outlined. A potential ‘geographical imagination’ of the sea, drawing on key themes in contemporary scholarship is then presented, and grounded in empirical research. It is argued that human–ocean relations should be a key feature of geographical research agendas.