Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Using snakes for the registration of topographic road database objects to ALS features

For historical reasons many national mapping agencies store their topographic data in a dual system consisting of a Digital Landscape Model (DLM) and a Digital Terrain Model (DTM). The DLM contains 2D vector data representing objects on the Earth’s surface, such as roads and rivers, whereas the DTM is a 2.5D representation of the related height information, often acquired by Airborne Laser Scanning (ALS). Today, many applications require reliable 3D topographic data. Therefore, it is advantageous to convert the dual system into a 3D DLM. However, as a result of different methods of acquisition, processing, and modelling, the registration of the two data sets often presents difficulties. Thus, a straightforward integration of the DTM and DLM might lead to inaccurate and semantically incorrect 3D objects.In this paper we propose a new method for the fusion of the two data sets that exploits parametric active contours (also called snakes), focusing on road networks. For that purpose, the roads from a DLM initialise the snakes, defining their topology and their internal energy, whereas ALS features exert external forces to the snake via the image energy. After the optimisation process the shape and position of the snakes should coincide with the ALS features. With respect to the robustness of the method several known modifications of snakes are combined in a consistent framework for DLM road network adaptation. One important modification redefines the standard internal energy and thus the geometrical model of the snake in order to prevent changes in shape or position not caused by significant features in the image energy. For this purpose, the initial shape is utilized creating template-like snakes with the ability of local adaptation. This is one crucial point towards the applicability of the entire method considering the strongly varying significance of the ALS features. Other concepts related to snakes are integrated which enable our method to model network and ribbon-like characteristics simultaneously. Additionally, besides ALS road features information about context objects, such as bridges and buildings, is introduced as part of the image energy to support the optimisation process. Meaningful examples are presented that emphasize and evaluate the applicability of the proposed method.     

ArcGIS在公众地理信息服务平台建设中的应用

在目前的GIS领域中,为用户提供及时、丰富、便捷的信息服务成为研究的主要课题之一。针对这种情况,本文主要阐述了怎样利用ArcGIS软件建立公众地理信息服务平台以体现地理信息的实效特征。     

浅谈新疆测震台网传输链路的改造

要做到IP到台站有很多种方式可选,根据新疆测震台网各台站的现状列举了7类不同的数据传输方式,这些数据传输方式在新疆测震台网中均得到应用,也都达到IP到台站的目的.分别对这些数据传输链路的原理及优缺点进行阐述,并结合目前各台站具体情况作链路适用性的相关分析,希望能够对其他省局台网的台站通信链路设计、改造起到参考.     

基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法

基于区域农村贫困程度测定方式不完善及缺乏地理视角的现状,选取四川省36个国家级扶贫县为实证对象,构建自然社会经济全面耦合的农村贫困测度指标体系,分析区域农村贫困的影响机制,并运用GIS与BP神经网络模拟区域自然致贫指数、社会致贫指数和经济消贫指数的空间分布格局。在此基础上,提出了全面表征区域农村贫困程度的区域扶贫压力指数——一种新的区域农村贫困测度方法,为国家扶贫政策文件《财政扶贫资金管理办法》中关于财政扶贫资金基于区域农村贫困程度分配提供实践基础。     

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.     

改进Moser法射线追踪

地震波场正演模拟、层析成像、偏移成像经常需要用到射线追踪,本文在基于图形理论的Ｍｏｓｅｒ法基础上,改进其网格节点的划分、追踪时路径的选取,并增加直射线追踪以消除在速度变化不大时射线受节点布置影响出现不合理的折曲现象．     

全球流体通道网

新近取得的深反射与全球地震层析成像资料,为地球内部结构和流体通道的研究提供了一些依据．通过研究这些地震资料,可以了解地球内部物质分布状态的几何模式．综合研究表明,地球内存在流体通道网,它连通地球外核、中幔圈、软流圈和岩石圈,是固体地球系统的重要组成部分．这种通道网络象“动脉”那样把地球外核中的流体和热量向外传送,但与热羽说不同的是,不见得有对应的“静脉”存在以保持地幔质量平衡．固体地球作为多层次多要素的巨型复杂系统,其动力学过程要用浑沌理论去解释．研究地球流体活动的轨迹和吸引子,将有助于深入了解地球活动的规律性．     

无锡光伏产业链中的全球-本地联系

以无锡市为例,通过实地调研当地光伏产业链中多晶硅生产、电池元件生产、组件制造、相关设备生产、光伏设备运维服务,以及光伏用户等不同环节,从全球生产网络的视角,考察全球-本地联系在当地光伏产业链形成演化和技术发展动态中的具体表现。结论指出：① 无锡光伏产业在短短10 a之内从“两头在外”的发展模式到形成全产业链竞争优势,体现了新兴技术的全球生产网络技术加速更新和产业快速转移的新特点;② 本地相关产业集群通过技术引进、消化和改进为新技术突破规模化瓶颈,获取市场竞争力打下基础,并由此对全球光伏技术发展的路径产生了关键影响;③ 产业进一步发展需要针对能源转型的长期目标,着重探索能源消费侧的技术转型路径。     

基于神经网络方法获得最优化月球内部结构模型

由于观测手段有限,目前对月球内部结构的认识还存在很大的不确定性,至今仍没有一个被广泛认可的内部结构模型,且现有对月球内部结构模型的研究几乎很少关注观测值对观测精度的影响.本研究采用混合密度神经网络方法得到了月球内部结构模型的后验概率密度分布,获得了平均月球内部结构模型(Mean模型)、最大后验概率对应的月球内部结构模型...