高光谱影像信息向量机分类

信息向量机是一种基于贝叶斯理论的稀疏高斯过程方法,其模型训练速度快、内存耗费小、稀疏性强,具有良好的预测性能。本文从高斯过程回归模型出发,提出了一种基于信息向量机的高光谱影像分类方法,针对高斯过程分类中的非高斯噪声模型,采用假定概率滤波算法将分类问题转化为回归问题,通过最大化边缘似然函数进行模型训练,选择活动子集中的信息向量数量达到了稀疏的目的。通过ROSIS影像试验,表明了基于信息向量机的高光谱影像分类方法的优势。     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

Cloud classification in NOAA AVHRR imageries using spectral and textural features

Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70–90%. Computer-based snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche forecasting etc.     

热红外遥感浙江地表热环境分布研究

全球气候变暖与人类戚戚相关,研究发现不仅是城市的热岛效应引起了局部区域的地表热环境分布差异,很多地质构造、岩性、土壤、植被等地质及自然地理因素也有影响。本研究选取浙江省作为研究区,利用Landsat 8 OLI/TIRS遥感影像反演地表温度,分析冬季地表热环境分布及其影响因子。结果表明,断裂带附近地表热环境受到断裂带分布影响;岩石和土壤通过不同的地表覆被类型影响地表温度。地表热环境分布与断裂带等自然因素存在一定相关性。     

支持向量机在建筑物变形监测中的应用探讨

论述了支持向量机的回归算法和基于时间序列支持向量回归的建筑物变形预测方法,并用MATLAB6.5工具箱编制基于时间序列支持向量回归程序,建立了基于时间序列支持向量回归模型。最后以实例数据讨证基于时间序列支持向量机回归模型的预测方法。研究表明:用时间序列支持向量回归模型建立的建筑物变形监测的预测模型是可行的和有效的。     

Landsat image enhancement and analysis approach for geological studies of central part of Goa,India

With the advent of multispectral scanners and the availability of digital data, information extraction through remote sensing has become one of the viable tools for studying natural resources. Normally thick vegetation and soil cover are common obstacles while geologically studying an area remotely. The study area, Goa, is largely covered by settlements, private mines, and dense vegetation. This makes it difficult to decipher lithology, structures and to find their extension by ground surveying. In this paper, an attempt has been made to study a variety of image enhancement and analysis techniques to delineate geological features, lineaments, and several landuse features. The information gathered from land use features and vegetation cover is also utilized in delineating lithology and lineaments. Landsat Multi-Spectral Scanner (MSS) data both in the visual and digital form have been used for the analysis. Various photographic techniques such as Bas-relief, combined printing of positive and negative for different bands, color composites, and digital image processing techniques like ratioing, principal component analysis and ratioing of the first two principal components have been applied for geological information extraction. This paper examines comparative utility of enhancement techniques in studying geological aspects. It is found that the ratio image of PCI and PC2 gives most significant and detailed information with maximum contrast and sharp boundaries. Bas-relief images are excellent for identifying geomorphic features and lineaments.     

利用SRTM-DEM和TM数据提取平原山地信息的研究

研究表明,基于SRTM-DEM派生的坡度模型进行平原、山地的自动提取具有很大的实效性,但坡度分级方法的选择及阈值的设定对提取结果具有较大影响;在此基础上,对区域植被、土壤、水文、地质等多地理要素信息及制图综合知识的综合运用,可大大提高基于知识的遥感专家修正精度;二者结合,初步实现了基于SRTM-DEM与遥感的平原、山地自动半自动提取,改进了传统手工和野外调查为主的提取方法,实现了地貌信息的数字化、定位化及定量化,为全国1∶100万数字地貌信息提取与集成奠定了基础。     

GIS支持下滑坡灾害空间预测方法研究

滑坡预测在防灾减灾工作中具有重要意义,它包括空间、时间预测两个方面。基于统计模型进行区域评价与空间预测是滑坡灾害研究的重要方向,但是预测结果往往依赖样本数量和空间分布等。本文以马来西亚金马伦高原为研究区,选择高程、坡度、坡向、地表曲率、构造、土地覆盖、地貌类型、道路和排水系统作为评价因子,探讨运用地理信息系统(GIS)和遥感(RS)获取与管理滑坡灾害信息,以及热带雨林地区湿热环境下滑坡空间预测的方法。支持向量机(SVM)和逻辑(Logistic)回归模型分别应用于滑坡空间预测,结果显示平均预测精度分别为95.9%和86.2%,SVM法具有较高的描述精度,值得推荐;同时,基于SVM模型的滑坡空间预测受样本影响较小,预测结果相对比较稳定,这对于滑坡灾害区域评价与预测的快速实现具有实际意义。     

Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping

Abstract

The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database. In order to run a series of logistic regression models, different random landslide-free sample sets were produced and combined with seed cells. Different susceptibility maps were created with an average success rate of nearly 80%. The coherence among the models showed spatial correlations greater than 90%. Models converged in the parameter selection peculiarly, in that the same nine of 20 were chosen by different logistic regression models. Among these nine parameters, lithology, geological structure (distance/density), landcover-landuse, and slope angle were common parameters selected by both the regression models and literature. Accuracy assessment of the logistic models was assessed by absolute methods. All models were field checked with the landslides resulting from the 12 November 1999, Kayna?li Earthquake (Ms = 7.2).     

无人机遥感在高山峡谷区崩塌地质调查中的应用

为了近距离调查高山峡谷地区崩塌地质灾害的发育情况,并快速提取灾害基本信息,利用无人机遥感对地形条件相当复杂的贵州大方县法启村火车站东侧崩塌群进行了详细的地质调查,探讨了在高山峡谷区利用地质灾害无人机遥感获取高分辨率影像的关键技术方法控制;并利用影像数据处理系统软件构建了DEM和DSM模型,通过中误差精度模型得到调查区模...     

Spatial prediction of landslide susceptibility by combining evidential belief function,logistic regression and logistic model tree

Abstract

In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models.     

机载LiDAR技术应用于茂密植被山区地质灾害调查

我国茂密植被山区地质灾害具有高位、高隐蔽性的特点,传统地质灾害排查手段在有效解决隐患的早期识别方面存在一定困难.机载雷达技术不仅可获取地面反射的三维激光点云,同时能够提供高分辨率、高精度的地形地貌二维影像.机载雷达的多次回波技术可"穿透"地面植被,通过滤波算法能够有效去除地表植被的影响,获取真地面高程数据信息,从而可获...     

黄土山区煤层群分层开采地表位移规律分析

为了研究黄土山区地形对煤层群开采地表位移的影响,以榆林某矿为地质原型,利用数值软件模拟地表为近水平、正坡、负坡、凸面和凹面五种地形条件下煤层群开采。结果表明:黄土山区地形的变化,对煤层群开采顶板垂直应力影响不明显,对地表位移影响较大;单向坡开采,斜坡滑移引起的下沉量在坡顶与开采沉陷量形成“叠加”,在坡底形成“抵消”;在组合坡中,煤层群开采地表位移量相差很大,说明在变坡点附近地表位移较为复杂。     

密集低矮植被区LiDAR点云地面滤波算法

针对地形复杂且低矮植被茂密的矿区LiDAR点云特点,本文提出了一种基于坡度信息并结合平面拟合的地面滤波算法。该方法采用二级格网法逐级选取地面种子点,在每个一级格网中,利用地面种子点通过最小二乘拟合法进行平面拟合并构建地面模型,最后达到区分地面点和非地面点的效果。与传统坡度法和布料模拟法的对比试验表明,该方法能够有效滤除密集低矮灌木,以及较好地保留较大坡度地形。     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

多源遥感数据支持下的区域性森林冠层高度估测

结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。     

基于遥感调查与GIS分析的林芝地区地质灾害评价

利用遥感、G IS技术和其它分析手段,在“递进分析法”(AMFP)理论框架下,利用AHP模型评估各影响因子权重;选用综合指数评价模型求取潜势度、危险度及危害度等区域地质灾害评价指数;借助自建的灾害评价系统,实现了藏东林芝地区的区域性地质灾害预测评价及其可视化表达。研究结果表明:该方法评价结果较为合理;研究方法和试点区预警系统的建设实践对于区域性灾害的预测、预报和防止不仅具有理论意义,也具有重要的现实意义;将人类活动等影响因子量化,不仅缩小了预测区的范围,也突出了地质灾害对人类生存环境的影响。     

CF耦合LR模型的地质灾害易发性评价

为对自然灾害频发的西北地区进行地质灾害易发性研究,本文考虑了高程、坡向等10类因素,利用确定性系数(CF)模型和确定性系数耦合逻辑回归(CF-LR)模型对城固县地质灾害易发性进行评价。结果表明,CF模型和CF-LR模型的灾害比均呈递增状态,高、极高灾害点密度分别为4.75、5.97个/km²,且两种模型的测试集受试者工作特征(ROC)曲线下面积(AUC)分别为0.812、0.835,验证集ROC和AUC值分别为0.862、0.891。两种模型均能有效评价区域内地质灾害易发性,且CF-LR模型具有更高的评价精度。     

基于GIS的贵州省地质灾害危险性评价

根据贵州省的地质条件,确定地质灾害分布、地形地貌、地层岩性、地质构造、河流水系、年均降雨量作为评价因子,采用黄金分割法确定各评价因子的权重,利用GIS技术和数学模型对贵州省地质灾害危险性进行评价,得到贵州省地质灾害危险性区划图。从图中分析得知贵州省区域地质灾害的危险性主要处于中度危险区,其次是低危险区和高危险区,处于极高危险区的相对较少。     

一种结合地理知识的遥感影像目标实体关联方法北大核心CSCD

遥感影像目标识别技术已广泛应用于目标动态监测与定位等领域。但影像目标识别的结果缺乏与目标属性信息的链接,导致分析人员只能依据影像特征进行分析,难以进行更复杂的目标数据关联分析与挖掘。针对遥感影像目标识别语义属性信息缺失的问题,本文利用知识图谱相关技术将影像判别的目标信息与知识语义网链接。首先,提出了一种遥感影像目标知识图谱构建框架;其次,针对遥感影像目标不同的数据类型,构建遥感影像目标知识抽取模型,提出了基于相似度目标实体识别和预定义模式的关系抽取方法;然后,基于多特征Logistic模型的影像目标实体链接方法,实现了遥感影像目标实体与百科知识库的知识关联;最后,针对预定试验区域进行试验,验证了本文方法的可行性。