支持向量机在遥感数据分类中的应用新进展

支持向量机是一种基于统计学习理论的新型机器学习算法,它通过解算最优化问题,在高维特征空间中寻找最优分类超平面,从而解决复杂数据的分类及回归问题.随着应用面的不断扩大,支持向量机在遥感领域也得到了广泛关注.该算法已经成功的应用于遥感数据的土地覆盖、土地利用分类,多时相遥感数据的变化检测,多源遥感数据信息融合等,并且在高光谱遥感数据处理中得到了广泛应用.综述了支持向量机算法在遥感数据分类中的应用.首先对支持向量机的理论进行简要介绍,进而综述了该算法在不同遥感问题中的应用进展,最后阐述了新型支持向量机算法的发展以及在遥感中的应用.     

结合PCA、多尺度分割及SVM的ASTER遥感蚀变信息提取

为了利用遥感影像进行更加精确的找矿预测,本文选择新疆东天山尾亚地区ASTER数据进行矿化蚀变信息提取方法研究.为了提高信息提取精度,本文提出了结合主成分分析(PCA)、多尺度分割和支持向量机(SVM)的遥感矿化蚀变信息提取方法.首先,分析ASTER数据的特征,选取各矿化蚀变信息的特征波段,对组合波段进行主成分分析,获得...     

机器学习在地震预测中的应用进展

机器学习(Machine Learning, ML),特别是深度学习(Deep Learning, DL),在最近几年发展迅速,在数据挖掘、计算机视觉、自然语言处理、数据特征提取和预测等方面的应用中取得了令人振奋的进展。地震预测是复杂、涉及面广、不成熟而且充满争议的科学问题;其发展受到尚不清楚的地震机理和孕震结构、不完备的观测数据与真伪不清的地震现象等方面的限制。但是,机器学习有可能改善复杂地震数据的挖掘和发现,推动地震预测科学的发展。本文回顾了机器学习在地震预测的应用,包括强震、强余震和岩石破裂失稳等方面的预测,并展望了机器学习在地震预测方面的研究趋势。     

基于自动机器学习的机场温度预报方法研究

利用2015年1月—2021年3月ECMWF细网格数值预报产品构建训练样本,使用自动机器学习方法构建乌鲁木齐机场温度预测模型.结果表明:(1)ECMWF模式直接输出的乌鲁木齐机场温度平均绝对误差为1.7℃,基于自动机器学习方法的Auto-sklearn模型和Auto-Keras模型能够改善模式直接输出的误差,使平均绝对...     

基于GIS的空间数据和空间数据库浅谈

在数字城市、数字省区等数字工程建设和各个部门信息化建设迅速开展的时候，大量甚至海量空间数据的存储和管理成为地理信息系统建设工作者们面临的重要问题，研究空间数据和空间数据库变得非常迫切。简要介绍了空间数据的概念、特征和空间数据结构模型，并对空间数据库的设计做了简单阐述。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.