Super-resolution reconstruction based on CNN: A case study of Jilin-1 multispectral data

MS or MS+PAN is usually applied separately in convolutional neural network(CNN) resolution reconstruction to obtain high-resolution MS images, but the difference between the two datasets is rarely studied. This paper introduced a dual-channel network and took MS and MS+PAN of Jilin-1 spectrum satellites as two datasets to evaluate the performance of CNN resolution reconstruction, and analyzed the difference with bicubic and GS methods. The result of CNN reconstruction shows that MS+PAN dataset performed better than MS, with about 6% improvement in spatial and spectral components, and the overall quality of MS+PAN dataset was slightly higher than that of MS dataset, with QNR from 0.955 9 to 0.958 4. The bicubic performed best in spectral components with the quality value of 0.017, and GS performed best in spatial components with the quality values of 0.044 3. CNN showed similar performance in spectral and spatial components with the two traditional methods and achieved the best overall quality with QNR value of 0.958 4.     

Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.     

基于最优极化特征组合的SAR影像湿地分类

快速、准确的湿地地物分类是实现湿地精准监测的基础。为进一步研究湿地地物显著极化特征对分类结果的影响,提出了基于最优极化特征组合的SAR影像湿地分类方法。该方法利用箱型图等方式,在特征选择因子等准则下从多种极化分解方法选择最优极化特征进行组合,并在此基础上实现分类。首先,为了简化极化合成孔径雷达（Polarimetric Synthetic Aperture Radar, PolSAR）影像并降低其斑点噪声,对互易处理后的极化SAR影像进行多视化和精致Lee滤波。然后,进行6种极化分解,得到多种极化特征。再之,利用箱型图、Cloude-Pottier平面散点图和均值散点图详尽分析上述极化特征和双台河口湿地典型地物散射机制间的相关性,并据此在特征选择因子、特征判断因子、H/α平面等和均值标准差的准则下选择最优极化特征组合。最后,以上述最优极化特征组合为输入,设计支持向量机（Support Vector Machine, SVM）分类器,实现湿地的最优分类。本文以辽宁省盘锦市辽河入海口双台河口湿地为例,采用2016年7月的C波段Radarsat-2全极化数据验证最优极化特征组合的有效性。结果表明：① Cloude-Pottier分解的H、A和α、MCSM （Multiple-Component Scattering Model ）分解的表面散射、Pauli分解的T₃₃与Yamaguchi3分解的表面散射和二面角散射为最优极化特征;② 使用最优极化特征组合不仅可以减少极化特征冗余,还可以提高各湿地地物的生产者精度、分类总精度及Kappa系数,其中各湿地地物的生产者精度提高1%~5%,分类的总精度可达到94.25%,Kappa系数达到93.63%。     

基于MDR/MFI标准的语义数据统一注册模型

为解决国际标准化组织提出的互操作性元模型框架(MFI)系列标准中,映射注册标准的映射度不能满足实际应用需要的问题,在MFI-10和MFI-12标准基础上,提出三层语义数据统一注册模型,包括底层数据源层、中间注册层和顶层全域共享数据模型层,对复杂映射类型进行抽取与定义,并扩充到映射注册元模型;融入映射、注册技术和MDR标准,并作为全域数据模型。石油领域分布式EP数据库与EPDM数据库等的语义注册的应用表明,语义数据统一注册模型能够灵活、规范地解决企业数据的共享问题。     

基于自标准数据和数据港口架构的数据共享技术

为满足复杂数据共享需求、减轻数据提供者负担,分析自标准与数据港口架构理论机器实现技术.将数据模式的概念引入自标准数据模型,根据NIEM原理,构建由公共数据模型、业务领域数据模型及编码表构成的自标准数据模型库;基于MFI-13表单注册标准,根据自标准数据模型对数据集进行注册,为实现软件之间的互操作对应生成元数据,并描述元数据遵循的语法及语义;为实现自标准数据的共享,提出基于数据港口架构的数据共享模型,并构建原型系统,证明其有效性和技术可行性.     

基于双重注意力机制的滑坡识别方法优化

随着计算机视觉技术的发展,通过卫星图像深度学习进行滑坡识别的研究正在逐步展开。通过引入双重注意力机制,提出了一种基于卷积神经网络的滑坡图像识别优化算法。基于统计的2 200张滑坡图像数据集,探讨了10种网络结构及4种注意力机制对滑坡识别结果的影响,并通过比例为4∶1的训练集和测试集进行滑坡识别,验证了本文方法的有效性。结果表明：ResNet结构相较于其他网络结构表现更为优秀,就该算例而言,ResNet-101结构具有最高的召回率、精确率和F1度量。融入了双重注意力机制的卷积神经网络相较于单个神经网络而言,滑坡识别的精确率更大,且滑坡边界分割结果更接近于真实的滑坡边界,其中,ResNet-101+DAN模型为最优模型。相较之下,单个神经网络无法克服图像噪声的影响,图像分割结果不佳。     

Design and realization of the drawing software for snow/ice stratigraphic profile

1　IntroductionSnow/icestratigraphicprofileisoneofthetraditionalandimportantresearchfieldsinglaciology (Qin 1 995 ;Xie 1 988) .Thesnow pitprofilesareobserveddirectlyinthefield ,andthestratigraphicprofilesoficecoresaredelineatedandcompiledindetailat1∶1scalethroughnakedeyesinlow temperaturelaboratory .Themajorstratigraphicchar acteristicsobservedincludingcrystalgrainsize,structureofdepthhoar,lighttransmis sion,meltphenomena,windcrustetc.(ShojiandLangway 1 989) ,arenecessarytobedelineatedindia…     

基于SAR和AIS的角度最近邻数据关联方法

提升海上态势感知能力是构建智慧海洋的重要环节。针对目前海上目标研究单源传感器存在感知盲区,多源传感器数据关联易受杂波干扰、在密集区表现不佳等问题,本文基于合成孔径雷达(SAR)和船舶自动识别系统(AIS)数据,提出一种抗干扰性强的角度最近邻数据关联方法,充分利用SAR与AIS船舶目标的空间角度关系,提高船舶目标在密集区域点迹关联的准确性。首先,对AIS数据进行时空滤波,实现数据粗关联,构建关联分析的数据候选集;然后,从时空数据的空间关系角度出发,在灰狼优化和匈牙利算法的启发下,利用点迹对特征向量矩阵进行运算,实现对多源空间数据的优化关联;最后结合数据几何关系对结果进行置信度评估。本文选取5幅SAR影像与AIS数据进行实验,并基于SAR影像数据及船舶轨迹点分布密度设计仿真实验,结果表明,本文所提出的角度最近邻数据关联方法,在密集分布情况下,关联精度为传统NN、GNN算法的3.62和4.61倍,运行时间为1.69 s,相较于NN算法仅增长1.36 s,仅占GNN运行时间的0.49%,在运行时间增长不大的情况下具有更强的抗干扰能力,在密集区域仍能取得较好的关联效果。     

Variations of snow temperature and their influence on snow cover physical parameters in the Western Tianshan Mountains, China

This article discussed about snow temperature variations and their impact on snow cover parameters. Automatic temperature recorders were used to sample at 10-minute intervals at the Tianshan Station for Snow-cover and Avalanche Research, Chinese Academy of Sciences. 10-layer snow temperature and the snow cover parameters were measured by the snow property analyzer (Snow Fork) in its Stable period, Interim period and Snow melting period. Results indicate that the amplitude of the diurnal fluctuation in the temperature during Snow melting period is 1.62 times greater than that during Stable period. Time up to the peak temperature at the snow surface lags behind the peak solar radiation by more than 2.5 hours, and lags behind the peak atmospheric temperature by more than 0.2 hours during all three periods. The optimal fitted function of snow temperature profile becomes more complicated from Stable period to Snow melting period. 22 h temperature profiles in Stable period are the optimal fitted by cubic polynomial equation. In Interim period and Snow melting period, temperature profiles are optimal fitted by exponential equation between sunset and sunrise, and by Fourier function when solar radiation is strong. The vertical gradient in the snow temperature reaches its maximum value at the snow surface for three periods. The peak of this maximum value occurs during Stable period, and is 4.46 times greater than during Interim period. The absolute value of temperature gradient is lower than 0.1°C cm⁻¹ for 30 cm beneath snow surface. Snow temperature and temperature gradient in Stable period∼Interim period indirectly cause increase (decrease) of snow density mainly by increasing (decreasing) permittivity. While it dramatically increases its water content to change its permittivity and snow density in Snow melting period.     

基于AJAX的数据分页的设计与实现

针对传统数据分页存在用户等待时间较长的问题,提出一种基于AJAX的数据分页方法,并成功地在设备管理系统的查询模块上得到应用,实际应用的结果表明,在现有的Web应用中采用AJAX技术,能减轻服务器压力,提高系统的响应速度,减少用户的等待时间。首先对比了传统Web和标准AJAX的交互过程,接着介绍了A-JAX概念及核心技术,同时给出了结合AJAX和J2EE技术实现数据分页的例子。     

基于勘查大数据和数据集市的锰矿床三维地质建模

以黔东北锰矿矿集区的李家湾-高地-道坨超大型"大塘坡式"锰矿床为例,探讨了在开展勘查地质大数据的特征和结构分析的基础上,厘定事实表和维度表,进而通过对勘查数据的收集、提取、ETL操作,并应用OWB工具建立锰矿山勘探数据的数据集市的思路与方法。进而探讨了以所建立的数据集市为数据支撑平台,建立黔东北超大型锰矿床高精度三维地质模型的途径。该矿床三维地质模型汇集了海量的空间数据与属性数据,是锰矿矿山异质异构勘查开发大数据的有效载体。基于该精细、全息的矿床三维地质模型,不但可以直观反映矿床和矿体特征,而且可以作为矿山管理及其深部和外围找矿预测的可视化分析工具。      

Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

The Karakoram Highway(KKH), a part of the China–Pakistan Economic Corridor(CPEC), is a major highway connecting northern Pakistan to China. The inventorying and analysis of landslides along KKH are challenging because of poor accessibility, vast study area, limited availability of ground-based datasets, and the complexity of landslide processes in the region. In order to preserve life, property, and infrastructure, and to enable the uninterrupted and efficient operation of the KKH, it is essential to strengthen measures for the prevention and control of geological disasters. In the present study, SBASInSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar) was used to process 150 scenes of Sentinel 1-A images in the year 2017 along the Karakoram Highway. A total of 762 landslides, including 57 complex landslides, 126 rock falls, 167 debris slides, and 412 unstable slopes, ranging in size between 0.0017 and 10.63 km2 were identified. Moreover, this study also gains an inventory of 40 active glacier movements in this region. Landslide categorization, displacements characteristics, spatial distribution, and their relationship with various contributing factors have been successfully investigated along the entire KKH using image interpretation and frequency-area statistics. The criteria adopted for landslides categorization is presented in the study. The results showed that the 2-D ground deformation derived in Hunza valley echoes well with the general regional landslides characteristics. The spatial distribution analysis revealed that there are clumped distributions of landslides in the Gaizi, Tashkurgan, and Khunjerab in China, as well as in Hunza valley, and north of Chilas city in Pakistan. Statistical results indicated that these landslides mainly occur on south-facing slopes with a slope angle of 20°– 45° and elevation relief of 550 – 2,100 m. Landslide development is also related to low vegetation cover and weathering effects in mountain gullies. Overall, our study provides scientific data support and theoretical references for prevention, control, and mitigation of geological disasters in the Karakoram region.     

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains,China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R~2=0.55 vs.R~2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.     

Watershed prioritization of Palar sub-watershed based on the morphometric and land use analysis

Morphometric analysis is an indispensable tool for hydrological investigation that involves the development and management of drainage basin. This study characterizes the micro watersheds in the Palar sub-watershed using morphometric analysis and assesses its risk by land use and land cover features in a particular micro watershed. Palar sub-watershed is divided into 6 micro watersheds for prioritization based on morphometric and land use analysis. Several morphometric parameters （linear, shape and relief） are determined from the drainage map; ranks are assigned based on their capacity to induce erodability and degradation. Final ranking is based on the composite index calculated from the sum of the ranks of each morphometric parameter. Morphometric analysis reveals micro watersheds 5 and 6 as most susceptible and 2 and 3 as low susceptible. Land use is mapped using IRS ID LISS III satellite data. The risk in terms of watershed degradation involved to each micro watershed is based on the ranks of each land use feature, obtained from a similar composite index as that of morphometric analysis. Land use analysis shows that micro watersheds 2 and 4 fall under high priority category while 5 and 6 under low priority category. Integration of the morphometric and land use analysis shows that only microwatershed 1 falls under the same category in both analyses. Control measures are suggested to contain degradation depending on its specific land use pattern and morphometric features. This study ean be used to prepare a comprehensive watershed plan for the development or for planning resource conservation strategies, by integrating land use features with the drainage characteristics of the region, in particular for a hill ecosystem as the prioritization is at micro level.     

基于地质大数据与构造语义约束的断层建模

断层作为一种重要的地质构造,在自然界中普遍存在。在地质体三维建模中,断层处理的难点主要在于数据获取不易。如今,在地质大数据条件下,可以利用不同地质语义约束来推测断层的产状。但由于断层构造往往十分复杂,要利用地质大数据对断层构造进行约束建模,需厘清断层的形态分类及其数据模型。通过研究断层的分类,提取出影响断层产状的要素,将这些要素作为断层数据模型的组成部分,在地质大数据挖掘中作为挖掘目标,形成断层地质语义信息表,为后续断层建模提供数据支撑。在此基础上,提出一种新的断层建模思路,即将断层看成一个整体,通过Apriori算法对断层地质语义信息表进行分类,找到每个断层的标准模型。然后,利用断层地质语义信息表对该断层模型进行几何形态约束,形成符合特定地质语义的断层模型。最后,通过断层牵引构造的平滑过渡,将断层镶嵌在已经建好的地层格架模型中,完成断层的三维建模。      

基于SAR影像后向散射特性的中俄黑龙江流域洪水监测

黑龙江（又称为阿穆尔河）是中国和俄罗斯之间的国际界河,近年来洪水事件频发,给流域内中俄两国带来巨大的人口伤亡和经济损失,加强该流域的洪水监测是两国面临的共同紧迫需求。传统的光学遥感影像受制于洪水期间多云多雨的天气状况,难以及时获得无云影像。本文充分利用全天候雷达数据的优势,提出了一种基于哨兵1号（Sentinel-1）合成孔径雷达数据监测大面积区域洪水的方法。通过Gamma分布和高斯分布拟合SAR影像后向散射系数的概率密度分布,迭代后验概率差值,自动获得全局阈值来分割初始的水体,基于辅助数据细化去除了初始水体中与水体相似的误分类型,并由形态学操作后处理提高了提取的洪水的均匀性。结果表明：(1)与传统的分割算法相比,本文提出的方法基于SAR影像后向散射系数的分布规律进行概率密度函数分段拟合,将全局统计划分为局部关系,显著地改善了常规分割算法在水体和非水体像素量级相差过大而表现不佳的情况;(2)研究获得了2017—2020年逐年的洪水分布,结果总体精度在87.78%～94.89%之间,Kappa系数在0.76～0.89之间;(3)特别是对于大面积半干旱地区,本文结合了后向散射特性、地形和其...     

Evolution process of urban spatial pattern in Hubei Province based on DMSP/OLS nighttime light data

This paper intends to investigate the urban spatial patterns of Hubei Province and its evolution from three different perspectives: urban nodes, urban connections and urban clusters. The research adopts nighttime light imagery of cities in Hubei Province, the viewpoint of ′point-axis-area′ in the ′point-axis system′ theory, and employs light index model, gravity model and social network analysis. The findings are as follows: 1) In terms of urban nodes, the urbanization process of Hubei has been carried out mainly on the basis of external expansion rather than internal increasing. The polarization trend of urban connection network is strengthening. 2) As for urban connections, the estimation of urban connections using light index model is capable of containing various actual flow, and the connections are getting increasingly closer. 3) In regard to urban groups, seven urban groups of varying sizes have formed. On that basis, three stable and relatively independent urban groups as the centers, namely Wuchang, Yichang and Xiangyang emerge as well. But the structures of ′Wuhan Metropolitan Area′, ′Yichang-Jingzhou-Jingmen City Group′ and ′Xiangyang-Shiyen-Suizhou City Group′, which are defined by local development strategy in Hubei Province, are different from the above three urban groups.     

Estimations of net primary productivity and evapotranspiration based on HJ-1A/B data in Jinggangshan city, China

Net primary productivity(NPP) and evapotranspiration(ET) are two key variables in the carbon and water cycles of terrestrial ecosystems.In this study,to test a newly developed NPP algorithm designed for HJ-1 A/B data and to evaluate the usage of HJ-1 A/B data in the quantitative assessment of environments,NPP and ET in Jinggangshan city,Jiangxi province,are calculated using HJ-1 A/B data.The results illustrate the following:(1) The NPP and ET in Jinggangshan city in 2010 both show obvious seasonal variation,with the highest values in summer and the lowest values in winter,and relatively higher values were observed in autumn than in spring.(2) The spatial pattern indicates that the annual NPP is high in the southern area in Jinggangshan city and low in the northern area.Additionally,high NPP is distributed in forests located in areas with high elevation,and low NPP is found in croplands at low elevations.ET has no significant north-south difference,with high values in the southeast and northwest and low values in the southwest,and high ET is distributed in forests at low elevations in contrast to low ET in forests in high-elevation areas and in cropland and shrub grassland in low-elevation areas.(3) Compared to the MODIS product,the range of HJ-1 NPP is larger,and the spatial pattern is more coincident with the topography.The range of HJ-1 ET is smaller than that of the MODIS product,and ET is underestimated to some extent but can reflect the effect of topography.This study suggests that the algorithm can be used to estimate NPP and ET in a subtropical monsoon climate if remotely sensed images with high spatial resolution are available.     

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.     

基于多源数据的谭家湾滑坡变形机制及稳定性评价

针对2016年5月发生于秭归县西北部的谭家湾滑坡,结合卫星遥感影像、现场勘查资料以及历史资料等多源数据,初步明确了滑坡的影响区域、特征及发生时序;综合采用钻探、槽探、物探等手段,开展室内外相关实验,明确了滑坡区的地层特性以及岩土体物理力学性质指标,通过分析该区裂缝位移及GPS数据,对该边坡的变形机制进行了探讨,并对该区稳定性进行了评价。结果表明:①谭家湾滑坡属于不规则"圈椅形"中型松散层的水库下降型滑坡,滑坡区的地表形态、地质构造及岩性等因素决定了滑坡的形成和发育,库水位和降雨的共同作用激励了滑坡的变形;②滑坡根据时序共分为3级滑体,总体呈现多次、多层、相互影响的演化特点,第三级滑体具有牵引式特征;③滑坡体内地下水位随库水位下降而下降,但下降速率缓于库水位,随之坡体内水力梯度和渗透力显著变大,此时碰到强降雨,将会导致坡体地下水赋存,岩土体软化,加剧滑坡变形,须施加必要的防护措施。④稳定性分析表明,该滑坡现处于临界稳定状态,一旦发生降雨和库水位变化,局部段可能发生失稳滑动。