结合多尺度纹理特征的遥感影像面向对象分类

地物具有多尺度的特点,单一尺度难以准确描述遥感影像包含的地物纹理信息。利用我国自行研发的高分一号遥感影像数据,采用灰度共生矩阵对第一主成分进行纹理特征提取,利用Jeffries-Matusit距离选择多尺度组合,并通过单一纹理结合多光谱数据的分类精度,以及纹理特征间的相关性,最终选择多尺度纹理特征组合进行面向对象分类。研究结果表明:结合多尺度纹理特征组合的面向对象GF-1影像分类能有效提取地物信息,总体分类精度达到81.75%,Kappa系数0.78。     

高维遥感图像的快速分类算法

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。     

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

中国版大洋洲地表覆盖产品研制

针对全球变化研究对大洋洲地表覆盖产品的需求,该文以2000年和2010年的Landsat卫星影像为数据源,提出了对大洋洲影像按照月份分组并进行样本采集与规则训练的方法,采用GLC树分类器进行自动分类,经过分类后处理和数据集成,完成了2000年和2010年两期、30m分辨率的大洋洲地表覆盖产品研制工作。利用高分辨率影像、实地采集照片等进行室内精度评定,该大洋洲地表覆盖产品的精度达到90%以上。     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.     

矿床成矿系列——五论矿床的成矿系列问题

矿床的成矿系列(简称成矿系列)是矿床学领域的一个理论性概念,由五级序次组成。矿床成矿系列是成矿系列的第二序次,是成矿系列的核心部分。矿床成矿系列的划分,以岩浆、沉积、变质、表生和流体(非岩浆-非变质成因流体)5种成矿作用为基本原则,以成矿地质环境为基础,结合成矿的时段与形成的矿床组合进行划分。本文对矿床成矿系列时空范围、时空组成结构、矿化强度与演化、成矿区带内不同矿床成矿系列之间的演化、叠加和复合作用及对指导找矿的意义进行了论述。     

煤田资料的铀矿二次开发技术及其找矿意义——以二连盆地ZS煤田铀矿点的发现为例

在中国北方主要盆地铀矿地质调查工作中,煤田自然伽马异常作为主要矿化信息和找矿线索,为一系列铀矿床(点)的快速发现奠定了坚实基础。为进一步提高煤田资料铀矿二次开发的高效性和准确性,运用水成铀矿理论,以中国二连盆地ZS煤矿区典型铀矿点的发现为例,初步探讨了煤田资料的铀矿二次开发技术,提出了"异常筛选与选区分析、原位验证与环境判别、等时地层格架建立与沉积体系分析、综合分析与靶区定位"四位一体的煤矿区铀矿地质调查模式。这对中国北方含煤盆地铀矿地质调查工作提供了新的找矿思路和技术路线,具有较重要的指导意义。     

利用监督分类快速提取分层设色图件中数据的方法

为了快速将分层设色图件中象元的颜色值转化为具有实际意义的属性标识或物理、化学参数值,从而提取分层设色图件中包含的数据,作者提出了根据不同颜色分量(RGB)构建颜色特征空间,基于最小距离监督分类方法快速提取分层设色图件中数据的方法。使用该方法对东半球海域海面水汽分布数据进行提取,取得了良好的效果。该方法可以广泛应用于物理海洋、环境科学、地质调查、地理信息系统建库等多个行业领域的数据提取工作中。