一种高光谱遥感图像快速谱聚类算法

为获得分类效果更优良的遥感图像分类方式并解决高光谱遥感图像分类运算速度缓慢的问题,集成Lanczos算法与谱聚类算法,探讨了高光谱遥感图像谱聚类算法应用于遥感图像分类的可行性,提出了一种面向高光谱遥感图像的快速谱聚类算法;通过对比美国圣地亚哥机场高光谱遥感图像K-均值算法与谱聚类算法的分类结果,发现面向高光谱遥感图像的谱聚类算法易于识别线性地物,且分类的速度能得到较大提升。     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

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.     

基于谱聚类算法的海底滑坡危险性评价

海底滑坡的危险性评价与分区,对海洋工程设施的选址和危险预防具有指导作用.本文基于无监督机器学习的谱聚类算法对黄河口埕岛海域展开了海底滑坡危险性评价,构建了输入参数为9、输出类别为4、核函数参数为0.08的海底滑坡危险性评价模型.使用该模型进行评价,将研究区分为了海底滑坡危险性高、较高、较低和低的区域.评价结果与地质环境...     

基于时空聚类的出租车载客热点区域挖掘研究

针对出租车运营过程缺少路径优化指导造成运营能力分布不均、空载率高的问题,本文以成都市安装有GPS设备的出租车所采集的轨迹数据为研究对象,以提高出租车效益为目标,采用了一种基于网格的出租车载客热点聚类算法,通过对出租车GPS轨迹数据进行处理和聚类分析,充分挖掘出租车载客热点区域,从而为出租车的运营者和管理者提供信息决策服务。     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

中国沿海小黄鱼种群动力学及其空间异质性研究

小黄鱼是我国近海四种最重要的经济鱼类之一,在过去的几十年中小黄鱼种群及其两个地理亚种群经历了巨大的变化。小黄鱼的种群动力学研究,对于维持这一重要渔业的可持续管理是至关重要的。目前仅有的两个小黄鱼种群动力学模型只涵盖了较短的时间,且没有关注其空间差异。本文研究了1968年至2015年间黄渤海和东海小黄鱼的种群动力学模型,包含和不包含空间分层结构的两种贝叶斯模型被用于分析其种群动力学的大尺度空间异质性。本文分析了不同的假设,来研究小黄鱼种群动力学潜在的变化趋势。研究结果表明小黄鱼种群动力学特征具有明显的时间和空间变化。种群的增长速度从20世纪八十年代开始增加,而可捕系数从1981年到2015年增加了两倍多。与黄渤海亚种群相比,东海的小黄鱼亚种群生长速度更快,遭受的捕捞压力也更大。基于最大可持续产量MSY的参考点表明,无论是整个小黄鱼种群还是两个亚种群,近年来都有非常高的过度捕捞风险。因此我国小黄鱼的渔业管理急需更加保守的管理策略,同时考虑其地域差异。本文所用的方法可以应用于其他种类的资源评估和渔业管理,尤其是具有空间异质性和数据有限的种类。     

模糊ISODATA聚类算法在声速剖面自动分类中的应用

依据中国海浅海区30′按月历史统计声速剖面数据,通过归一化处理和Akima差值采样得到梯度剖面,建立起各方区按月归化后的声速剖面分层梯度样本集,并采用模糊ISODATA聚类算法对声速剖面进行聚类分析.通过对分类结果和类内总方差和的分析表明,聚类参数m值在1.1～2.1之间,并以最远邻系统聚类法结果为初始类中心的模糊分类效果较好.应用该方法对海洋中的声速剖面进行自动分类和区划对海洋环境的战术应用意义重大.     

基于高斯混合模型的海冰图像非监督聚类分割研究

为了利用海冰图像识别技术获取海冰冰况信息, 探索了利用高斯混合模型进行海冰图像分割的技术途径, 描述了具体算法, 并利用高斯混合模型的最大期望值（EM）算法以及最小描述长度（MDL）准则对渤海海冰图像进行目标提取。研究结果表明, 该方法可以很好地实现海冰信息的有效提取和海冰图像的有效分割, 从而证明了建立在图像分割技术...