基于条件生成对抗网络的电子地图可视水印去除方法

水印对于电子地图版权的保护十分重要,开展可视水印的去除研究有助于从攻防对抗的角度评价可视水印的有效性,进而提高电子地图可视水印的抗攻击研究水平。针对已有基于深度学习的可视水印去除方法存在训练样本量大、效率低、可视水印去除后有残留或要素丢失等问题,本文提出一种基于条件生成对抗网络(CGAN)的电子地图可视水印去除方法。该方法的模型主要由一个生成器和一个判别器组成,其中生成器采用U-Net结构,保证去除可视水印后生成的地图区域的真实性,判别器则采用基于区域判别的全卷积网络,通过对抗训练来区分生成的地图瓦片与真实地图瓦片之间的差异,使得可视水印去除后重建的电子地图更接近真实地图。实验表明,本文提出的方法简单易行,模型训练速度比基于全卷积网络的算法快4倍,能够去除文字、彩色图像及二者混合等模式的可视水印,可实现Google、高德、百度等国内外地图厂商提供的导航电子地图、遥感影像等多种地图瓦片上的可视水印批量去除,无需人工干预,且水印去除后的地图瓦片与原始真实地图瓦片有着良好的结构相似性。     

基于生态优先的海洋空间资源高质量开发利用对策研究

海洋空间资源开发利用质量与政府公共资源规划配置决策、资源使用者利用方式密切相关。文章通过对目前海洋空间资源开发利用特征研究,揭示了影响海洋资源开发质量的资源储备、规划配置、生态环境、开发方式等方面的制约因素,并从政府机制建设和使用者资源开发模式两个层面提出完善和改进对策。一是政府在海洋资源收储、统筹集约规划、生态环境保护方面建立保障与引导机制;二是资源使用者选择技术创新和精细化开发策略,以期为实现海洋空间资源高质量开发利用提供参考。     

基于空间遥感影像的居民地自动解译

针对当前地物要素信息采集劳动强度大、智能化程度不高、效率低等技术瓶颈,本文以深度学习理论为基础,在Caffe框架上依托Digits网络服务器构建居民地数据集进行分类识别训练,建立样本数据集并完成模型训练工作.整合居民地复杂数据信息,设计了针对遥感影像自动解译居民地的操作流程.通过对实验结果分析,利用深度学习获取的数据模...     

基于深度学习的地震与爆破事件自动识别研究

针对天然地震事件、爆破事件分类问题,使用甘肃及周边地区80个天然地震事件和20个爆破事件建立数据集,采取深度学习卷积神经网络(convolutional neural network,CNN)方法搭建两个不同结构的模型进行训练,并用500条训练集之外的天然地震事件与爆破事件波形作为测试数据集,其训练和测试准确率均达到9...     

Rapid estimation of soil heavy metal nickel content based on optimized screening of near-infrared spectral bands

In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils.     

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.     

机器学习在地震紧急预警系统震级预估中的应用

地震预警是地震减灾工作的重要途径,而震级预估是整个地震紧急预警系统中重要且较为困难的一个环节.目前,世界上多个国家和地区都已建立了各自的地震预警系统,并且形成了特征频率(τ_p和τ_c等)相关和特征振幅(Pd等)相关的两类震级紧急预警的方法,但各有局限性.本文在已有的方法和理论基础上,运用机器学习算法,将日本KIK和KNET台网从2015年至2017年所记录到的843条地震目录,55426条记录作为全数据集,设计、训练出一套用于常见震级范围的机器学习震级预估模型.与已有方法的预估结果相比,机器学习方法不仅使预估的整体误差和方差下降,同时多台联合评估单一地震事件的截面方差也更低.本研究的结果显示了机器学习算法在震级紧急预估问题上具有较广阔的应用前景,同时也为较为复杂的深度学习类算法框架下端到端模型提供了实践基础和研究可能.     

山区的山地经济协同开发研究——以重庆市三峡库区为例

山区是生态环境和经济社会发展问题集中的区域。以国家有关资源型城市发展、重庆市近年来区域经济城乡统筹发展、三峡库区治理贯彻实施的大政方针为宏观背景,以三峡库区山地资源系统开发为个案,并将该区域资源状况进行统计学意义上的分析综合,从具体到抽象,再从抽象到具体,为我国山地资源系统开发与山地区域经济发展的科学整合做了探索性研究。最后,有针对性地提出了相应对策措施。     

Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data

The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs. Developed ELM models were compared with the artificial neural networks (ANN) and radial basis function (RBF) models. The models were also compared with the autoregressive moving average (ARMA), and evaluated using mean square errors, mean absolute error, Nash-Sutcliffe efficiency and determination coefficient statistics. All the data-driven models had better accuracy than the ARMA, and the ELM model’s performance was superior to that of the ANN and RBF models in modelling 1-, 2- and 3-month-ahead GWL. The RMSE accuracy of the ANN model was increased by 37, 34 and 52% using ELM for the 1-, 2- and 3-month-ahead forecasts, respectively. The accuracy of the ELM models was found to be less sensitive to increasing lead time.     

1952年-2010年中国人口分布与社会经济发展的时空耦合特征分析

人口经济的时空耦合态势是中国区域社会经济发展空间均衡过程的重要体现,可有效地指导中国经济布局、民政建设、交通发展和环境管理等相关政策和措施的制定和实施。在地理信息系统（GIS）的支持下,采用人口分布结构指数、人口密度基尼系数、人口经济重心迁移、空间自相关分析等方法,对1935年-2010年6期县级行政单元人口普查统计数据进行空间插值,实现人口密度分界线的空间刻画及其变化趋势分析,结合1952年-2010年省级行政单元人口、经济统计数据,从人口与经济重心空间耦合及其空间一致性两个角度来揭示社会经济与人口分布的时空差异,剖析人口分布与社会经济的联系。得出以下结论：（1）由于社会历史、自然环境和经济条件的影响,中国人口空间分布的时空演变特征不尽相同,东西部差异较大,且不均衡态势日益明显。（2）胡焕庸线至今仍能很好的概括中国人口东南地狭人稠、西北地广人稀的空间分布格局,但是甘肃、宁夏、内蒙古地区的人口分布已经在局部范围内突破了胡焕庸线的限制,中国人口分布在一定程度上呈现向西部转移的趋势。（3）中国人口分布与经济发展空间一致性的地域分异特征十分明显,但省际一致性在不断缩小,人口集聚具有较强的经济导向性。（4）全国和东部、中部、西部和东北部四大区域在1952年-2010年的人口经济一致性程度及其变化趋势各异：全国人口经济空间一致性程度相对较低,东北地区人口经济一致性程度较高且还在改善,东部与中部保持稳定,西部较差且呈减弱趋势特征。