MASCULINISM,POWER, AND DISCOURSES OF EXCLUSION IN BRIAN BERRY’S “SCIENTIFIC” GEOGRAPHY

This paper examines the impact of changes in industry mix, changes in technology, differential plant growth, plant entry and plant exit on metropolitan labor productivity growth between 1963 and 1997. Analysis is based upon unpublished plant-level data from the United States Bureau of the Census. We show that manufacturing productivity varies markedly between metropolitan areas in the United States. The most influential components of productivity growth are technological changes within incumbent plants and changes in industry mix. Significant differences in the relative contributions of these components of productivity change exist across metropolitan areas. Regional differences in rates of plant openings and plant closures also exert considerable impact on metropolitan productivity improvement.     

Texture augmented detection of macrophyte species using decision trees

Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.     

面向对象分类特征优化选取方法及其应用

与传统基于像元的分类方法比较,面向对象的分类方法可利用的地物信息更加丰富,然而如何从众多信息中筛选出能够有效提取不同地物的分类特征,从而提高分类效率与精度,是使用面向对象方法分类时急需解决的问题。SEaTH算法（分离阈值法）是一种有效的自动选取分类特征并计算阈值的方法,但其只考虑了类间距离,容易存在信息的冗余,从而对分类精度造成一定影响。本文在SEaTH算法的基础上,综合考虑了特征间的相关性、类间距离以及类内距离,对SEaTH算法进行了优化,并将改进前后的两种方法运用到广东省肇庆市TM影像及环境一号卫星影像土地覆盖分类中进行对比分析。实验结果表明,改进后的方法筛选出的特征在提取地物上更为有效,尤其使耕地的分类精度提高了12.26%,使分类总体精度由80%提高到了85.26%。改进后的方法对不易获取多时相影像的地区的土地覆盖分类具有重要意义。     

A fast clonal selection algorithm for feature selection in hyperspectral imagery

Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.     

Model generalization of two different drainage patterns by self-organizing maps

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.     

若尔盖铀矿田方解石稀土元素与碳氧同位素地球化学特征及其意义

方解石是若尔盖铀矿田与成矿最为密切的脉石矿物之一。方解石的REE地球化学特征研究表明,产于地层中的方解石、矿区中的方解石和含矿方解石具有明显不同的稀土元素组成特征,分别具有轻稀土富集右倾型、重稀土富集左倾型和相对平坦型的3种稀土配分模式。其中产于地层中的方解石明显继承了地层中岩石的稀土元素特征,而含矿方解石表现出与矿石稀土元素相似的特征。碳氧同位素显示地层中的方解石为海洋沉积碳酸盐岩的碳氧同位素组成特征;矿区方解石脉、含矿方解石脉的碳同位素组成明显表现为地幔来源的特点;而矿区方解石脉的氧同位素组成具明显的深部来源特征,含矿方解石脉的氧同位素组成表明在成矿过程中有大气降水的混入。若尔盖铀矿田的方解石主要为热液成因,其矿床成因类型属于典型的热液矿床,成矿流体主要来源于地幔。     

准噶尔盆地中部1区钻井优化设计研究

围绕钻井工程设计优化展开研究,论述了准噶尔盆地中部1区井身结构优化过程,提出了井身结构优化方案;建立了该区岩石可钻性剖面,结合钻头使用效果评价,推荐了钻头选型方案;介绍了该区钻井液体系优选及使用情况。形成了准噶尔盆地中部1区钻井设计优化方案,提出了后续研究方向。     

河南濮城油田沙二上2+3砂层组末端扇沉积特征及剩余油分布

本文以濮城油田沙二上2+3砂层组为例,分析了末端扇储层的沉积特征,建立了末端扇沉积模式,确定了剩余油分布.末端扇储层分为近端亚相、中部亚相和远端亚相,中部亚相的分流河道微相是主要储集砂体.剩余油平面上主要集中在分流河道侧翼过渡区及近水道漫溢、远水道漫溢和分散的井网难控制的小透境体中,垂向上集中在正韵律顶部和反韵律底部等物性差的地方.二砂组主要富集在S23、S24小层的近气顶区,三砂组主要富集在S34、S35小层的濮53、文17断块构造高部位及注采不完善的地区.     

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.