山区植被类型信息提取方法研究

根据遥感图像的光谱信息和空间信息特征及不同植被的分布规律，研究利用计算机处理技术提取山区植被类型的方法。分类过程采用四个步骤完成：①均一目标的象限四分树提取分类；②多光谱数据的最小距离分类；③综合利用波谱曲线的形态和地形数据进行分类；④高程数据修正分类。在分类处理过程中，分别利用了图像的空间信息、光谱信息以及地形数据。利用该分类方法在实验小区内进行植被类型提取试验，其精度为９０％。与最大似然分类方法所得结果相比较，其分类精度提高了１０％。

冀东地区金矿地质遥感信息提取方法研究

冀东地区金矿主要包括金厂峪、长城式和峪耳崖型。后期叠加断裂构造往往直接控制矿体产出，黄铁矿化是矿化主要标志。根据光谱特征可把金矿区围岩、蚀变岩和矿脉分开。位于断裂附近的羟基和３价铁信息复合异常地带是金矿化远景区。热红外多光谱资料对岩性和构造识别有明显效果，可以进行变质岩与白云岩的区分和区内主要出露岩类的区分。区域性断裂—线性构造。

伏安法实验数据子波信息提取方法的研究

从实际测得的伏安法实验数据出发 ,分析研究了伏安法标准加入浓度曲线 (子波 )的提取方法。从分析子波畸变的特征入手 ,结合基于奇异值分解原理的TLS算法 ,提出了一种两组份浓度子波峰形的相关滤波叠代求解方案。

利用遥感影像自动提取变化信息的方法研究

目前土地利用遥感动态监测已进入实用化阶段,但其自动化程度不高,影响作业效率。本文在使用增强法对两时相的遥感影像自动发现变化基础上,结合两时相影像的分类结果,对变化图斑进行类别标记,然后对标记图像进行矢量化、变化信息提取和统计,从而提高变化监测工作中的自动化程度、减轻工作量。

基于SPOT、TM图像的土地利用变化信息提取

在土地利用动态遥感监测中，变化信息提取关键问题。本文讨论了运用光谱特征变异法等算法，将宁波市不同时期的SPOT、TM多个图像的光谱信息进行分析、处理与比较，并结合目视判解译，获得2001年度宁波市土地利用变化信息的方法。

基于数字化技术的铁路路基三维信息模型算量方法研究

路基工程在铁路建设中非常重要,其数量直接影响到铁路建设的投资,因此精确计算路基工程数量非常关键。传统二维设计计算工程数量是根据实测横断面采用平均距离断面法,未有效地根据地形变化对数量进行实时交互计算,导致数量与现场实测值有一定差别,尤其在地形起伏变化较大的丘陵地区差别更为显著。基于数字化技术及多专业协同技术形成的三维设计模型,实现了全方位、多角度查看、构件单元快速识别、构件与地形面数据化交互,从而使设计精度更高,提取的数量相对更精确。以某高速铁路工程为例,详细介绍铁路路基三维信息模型算量方法在铁路正向设计中的应用,将传统二维设计算量与断面法和基于数字化技术的三维信息提取法算量、网格划分法计算土石方数量做了详细对比并得出具体指标值。通过对比分析,总结铁路路基三维信息模型算量的优劣。与传统二维设计算量相比,基于数字化技术的三维信息提取算量方法具有更高的精度、更快的计算速度和更强的交互性,可以在铁路正向设计中发挥重要作用;提出针对不同类型构件、土石方分别采用信息提取法、网格划分法的一整套路基三维信息模型算量方法,大大提高工程算量精度,计算结果更准确。

期刊博览

基于多时相HJ卫星的冬小麦面积提取 我国环境与灾害监测预报小卫星HJ-1A／B具有较高的时间和空间分辨率，在作物种植面积提取和长势监测等方面具有较大优势。本文根据冬小麦的物候规律和季相节律的差异性，选取返青期和拔节期两个生育期的HJ卫星影像，借鉴分层信息提取法原理，综合利用监督分类和非监督分类法，结合人及交互目视解译和实地定位调查等资料提取了姜堰市的冬小麦种植面积，总体面积提取精度达到90．22％，样点空间匹配精度为81．25％，

关于交通事故车速鉴定研究

交通事故车速鉴定是交通事故重要的技术手段,对于事故责任认定及赔偿标准的确定有着重要的意义。本文首先介绍了交通事故车速鉴定的定义和基本原理,以及其在交通事故中的作用和意义。接着,详细介绍了交通事故车速鉴定的三种主要方法:现场勘查法、车辆碰撞模拟法和车辆信息提取法。同时,对误差来源和减少方法进行了分析和探讨。最后,文章总结了交通事故车速鉴定的现状和未来研究方向。

Fast Texture Segmentation Based on Semi-Local Region Descriptor and Active Contour

In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images composed of both homogeneous and textured regions. Because these images cannot be in general directly processed by the gray-level information, we propose a new texture descriptor which intrinsically defines the geometry of textures using semi-local image information and tools from differential geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The existence of a minimizing solution to the proposed segmentation model is proven. Finally, a texture segmentation algorithm based on the Split-Bregrnan method is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.

A Preliminary Global Analysis of Remotely Sensed Radiation Characteristics of Ground Features: Ⅰ. Methodology and Results

A Landsat data transformation method which was proposed by the author was applied to extract useful information from data of 769 ground feature classification units of worldwide scope.Three most important characteristic values--the general radiance level L,the visible-infrared radiation balance B and the band radiance variation vector (direction and speed) V were calculated.Then the 769 class units were sorted into 106 groups based on their natural characteristics.The means and standard deviations of L,B and V values for all the groups were calculated.The distributions of the 106 groups or the 769 units on the number axes of L,B and V,in the planes of L-B,L-V and B-V,and in the space of L-B-V were investigated.Finally,the typical numerical characteristics of the various ground features are discussed in consideration of their worldwide variations in the present paper.

Field analysis of metallogenic information and its application

Focusing on the issue to deal with inadequate extraction of metallogenic information especially geological information,a new method of extracting metallogenic information based on field model,i.e.the field analysis method of metallogenic information,was proposed.In addition,a case study by using the method of the extraction of metallogenic information from the west Guangxi and southeast Yunnan district as an example was performed.The representation method for the field models of metallogenic information,including the metallogenic influence field model and the metallogenic distance field model,was discussed by introducing the concept of the field theory,based on the characteristic analysis of the distance gradualness and the influence superposition of metallogenic information.According to the field theory superposition principle and the spatial distance analysis method,the mathematical models for the metallogenic influence field and the metallogenic distance field of point,line and area geological bodies were derived out by using parameter equation and calculus.Based on the metallogenic background analysis,the metallogenic information field models of synsedimentary faults and manganese sedimentary basins were built.The relationship between the metallogenic information fields and the manganese mineralization distribution was also investigated by using the method of metallogenic information field analysis.The instance study indicates that the proposed method of metallogenic information field analysis is valid and useful for extracting the ore-controlling information of synsedimentary faults and manganese sedimentary basins in the study area,with which the extraction results are significant both statistically and geologically.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

Classification Method Research to Remote Sensing Images

With rapid development of remote sensing technology, the resolution of remote sensing images is increasingly improved; then people can extract more useful data and information from these images. Thus, an important information extraction method from remote sensing images - image classification, becomes more and more important. Based on phenopthase and band composition characteristics, this paper firstly discusses the important role of background parameters in remote sensing images classification; then based on geographical infomation system technology, the computerized automatic classification to high-medium-low-yield croplands in Dingxiang County of Shanxi Province in rotate sensing images has been carried out by using eompound layers classification method of multi-thematic information; compared the classification result to the visual interpretation results, the accuracy increases from 70% to above 90%.

Extracting Information on Implied Volatilities and Discrete Dividends From American Option Prices

This paper deals with options on assets, such as stocks or indexes, which pay cash dividends. Pricing methods which consider discrete dividends are usually computationally expensive and become infeasible when one considers multiple dividends paid during the option lifetime. This is the case of long-term options and options on indexes. The first purpose of this paper is to assess efficient and accurate numerical procedures which yield consistent prices for both European and American options when the underlying asset pays discrete dividends. The authors then analyze some methodologies to extract information on implied volatilities and dividends from quoted option prices. Implied dividends can also be computed using a modified version of the well-known put-call parity relationship. This technique is straightforward, nevertheless, its use is limited to European options, and when dealing with equities, most traded options are of American type. As an alternative, the numerical inversion of pricing methods, such as efficient interpolated binomial method, can be used. This paper applies different procedures to obtain implied volatilities and dividends of listed stocks of the Italian derivatives market （IDEM）.

Modification of evidence theory based on feature extraction

Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict information exists. Many modification methods have been developed which can be classified into the following two kinds of ideas, either modifying the combination rules or modifying the evidence sources. In order to make the modification more reasonable and more effective, this paper gives a thorough analysis of some typical existing modification methods firstly, and then extracts the intrinsic feature of the evidence sources by using evidence distance theory. Based on the extracted features, two modified plans of evidence theory according to the corresponding modification ideas have been proposed. The results of numerical examples prove the good performance of the plans when combining evidence sources with high conflict information.

A novel approach for agent ontology and its application in question answering

The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web information and extracts answers on the basis of semantics. With SWAO method as the clue,the following technologies were studied:the method of concept extraction based on semantic term mining,agent ontology construction method on account of multi-points and the answer extraction in view of semantic inference. Meanwhile,the structural model of the question answering system applying ontology was presented,which adopts OWL language to describe domain knowledge from where QA system infers and extracts answers by Jena inference engine. In the system testing,the precision rate reaches 86%,and the recalling rate is 93%. The experimental results prove that it is feasible to use the method to develop a question answering system,which is valuable for further study in more depth.

Research on A Web Intelligent Information Extraction Method

The paper introduce segmentation ideas in the pretreatment process of web page. By page segmentation technique to extract the accurate information in the extract region, the region was processed to extract according to the rules of ontology extraction, and ultimately get the information you need. Through experiments on two real datasets and compare with related work, experimental results show that this method can achieve good extraction results.

Object-oriented Method for Rural Residential Land Extraction in the Hilly Areas of Southern China Using RapidEye Data

The process of rapid urbanization in China features two opposing trends： declining rural population and increasing rural residential land, especially in southern hilly areas. The extraction and analysis of residential land in rural China represents an important application for remote sensing technology. The study aimed to discover rural residential land information using RapidEye satellite imagery, taking Taihe County as the research area in the hilly region of southern China. Based on multiple experiments, classification was conducted with an optimal image segmentation scale set to 200. The object-oriented classification rule set was constructed using the customized parameters NDVI, NDWI, brightness, and length/width. The areas of residential land and other land use types were interpreted by varying the parameter values for classification rule sets. Finally, validation and accuracy evaluations were carried out. The overall accuracy of residential land interpretation is 78.40%, and producer＇s accuracy and user＇s accuracy are 68.75% and 77.33%, respectively. The results indicate that RapidEye provides a suitable data source for extraction of rural residential land using an object-oriented approach. Compared with the second national land survey of China, the classification gave an absolute difference of 93.67 ha residential land within the study area. Recognition errors occurred mainly in regions adjacent to the boundaries between residential land and other types of land.