面向对象的高分辨率遥感影像道路损毁识别研究

道路损毁信息的在灾后高效救援中发挥着重要作用。本文针对高分辨率遥感影像空间特征丰富、光谱分辨率低的特点,基于面向对象遥感影像分类方法,采用eCognition软件对芦山震后0.2m航空遥感影像进行道路损毁信息的提取实验。实验中结合GIS信息,首先将基于现有的道路矢量线文件生成道路矢量面文件参与分割,生成单独的道路区域对象以用于进一步处理。分割采用棋盘分割和多尺度分割,根据损毁道路影像特征创建波段比值,使用Assign算法和模糊分类算法提取出道路线、植被、损毁区和未损毁区,并将结果输出为矢量文件。根据损毁区损毁对象的周长、面积与道路宽度的关系,建立判定条件来初步判定损毁类型。最终将损毁粗判定结果与损毁路段的目视解译结果对比分析,检验该方法的准确率及可行性。结果分析表明上述方法能有效地识别出高分辨率遥感影像损毁道路信息。     

面向对象的eCognition遥感影像分类识别技术应用

以位于印度尼西亚Kalimantan热带雨林地区的1幅具有3个通道的雷达图像为研究对象,利用面向对象方法对高分辨率遥感影像的水体进行提取,在分割的基础上,经过分层分类,充分利用影像的光谱信息、拓扑信息、形状特征等构建知识库进行分类,实验结果理想,相对传统基于像素的图像分类分析方法结果图斑完整,提高了分类精度。     

基于面向对象方法的高分辨率遥感影像道路提取方法研究

随着遥感影像分辨率的不断提高,基于高分辨率遥感影像的目标自动提取逐步成为研究热点。本文采用面向对象的图像分析方法,基于Ecognition遥感图像处理平台,对IKONOS影像进行道路提取实验,重点对图像分割方案、道路提取规则、后处理方法等进行探讨。     

面向对象的高分辨率影像农用地分类

采用面向对象的影像分类方法,结合多尺度分割技术,以QuickBird影像为实验数据,进行农用地的精细自动分类。首先,根据地物大小,选择最优分割尺度,构建多尺度分割等级网;然后,综合利用高分辨率影像的光谱信息、纹理和形状特征,建立各个对象的特征集;最后,通过目视解译建立隶属度函数,实现地物的分层提取。实验表明,该方法能有效区分农作物种类,相对于传统的像素级分类方法,该方法明显提高了高分辨率影像的分类精度,且避免了"椒盐"噪声的产生。     

面向对象的高分辨率遥感影像道路损毁识别研究北大核心CSCD

道路损毁信息在灾后高效救援中发挥着重要作用。本文针对高分辨率遥感影像空间特征丰富、光谱分辨率低的特点,基于面向对象遥感影像分类方法,采用e Cognition软件对芦山震后0.2 m航空遥感影像进行了道路损毁信息的提取试验。试验中结合GIS信息,首先将基于现有的道路矢量线文件生成的道路矢量面文件参与分割,生成单独的道路区域对象以用于进一步处理。分割采用棋盘分割和多尺度分割,根据损毁道路影像特征创建波段比值,使用Assign算法和模糊分类算法提取出道路线、植被、损毁区和未损毁区,并将结果输出为矢量文件。根据损毁区损毁对象的周长、面积与道路宽度的关系,建立判定条件来初步判定损毁类型。最终将损毁粗判定结果与损毁路段的目视解译结果对比分析,检验该方法的准确率及可行性。结果分析表明上述方法能有效地识别出高分辨率遥感影像损毁道路信息。     

面向对象的高分辨率影像单值分类耕地提取方法研究

遥感技术已广泛地应用于土地覆盖/土地利用分类中。在专题应用中,用户只对某一类地物感兴趣,例如耕地提取,即单类别分类问题。随着影像分辨率的提高,基于像元的分类算法难以满足高分辨率影像高精度信息提取的需求。本文采用结合面向对象分类思想和基于正样本、未标记样本遥感单值（PUL）分类方法从多源高分辨率影像中提取耕地信息,并与基于像素的分类实验进行对比分析。结果表明,在缺少部分地类的不完全训练集下,基于面向对象的单值分类较传统神经网络分类有更较稳定的表现,并且远优于基于像素的分类结果。     

基于面向对象的珲春地区高分辨率遥感影像分类研究

以吉林省珲春市春化镇为研究区,以Pleiades、高分一号、资源三号影像为实验数据,利用面向对象信息提取方法实现了对3种遥感影像进行信息提取。利用3D Filter边缘检测算子对多尺度分割进行优化,通过对影像进行多次实验得出地物要素的最优分割参数,并且建立不同地物要素的分割层级。分析实验数据的特点构建了合理的分类层级,选取能区分各个地物要素的特征进行组合,利用阈值分类和模糊分类实现地物要素的信息提取。利用混淆矩阵对数据进行客观分析,得到3种影像的总体分类精度和kappa系数。分析结果表明:Pleiades影像分类精度较高,更适合本实验区的遥感影像信息提取。     

利用IKONOS影像生产1:10 000测绘产品

本文简要介绍了IKONOS卫星遥感影像适用于生产测绘产品的特点,总结了利用IKONOS影像进行1∶10 000测绘产品生产试验的主要结论,分析了利用IKONOS影像作为数据源实际生产1∶10 000测绘产品的情况。     

基于IKONOS影像的土地利用现状更新调查方法

近年来,随着我国社会和经济的快速发展,土地利用现状发生了很大变化,原有土地利用资料已难以满足土地管理的需求。为了提高土地管理效率和水平,实现科学化管理,结合地方规划与国土资源管理现状,采用现代"3S"技术,建立集遥感信息以及土地利用信息于一体的土地利用数据库,为地方经济发展提供现势、详实、准确的科学依据,为国土资源规划、保护、管理和合理利用奠定坚实的基础。结合银川市土地利用现状更新调查项目,论述利用高分辨率卫星影像数据——地面分辨率达1m的IKONOS影像进行土地利用现状更新调查的技术方法。     

利用MRF方法的高分辨率影像道路提取

高分辨率遥感影像提供了地物更丰富的信息,包括光谱信息、地物结构、形状、纹理以及地物之间的空间关系等多方面的信息。面向对象的影像分析方法以目标地物为研究对象,充分考虑目标地物的形状、结构及空间关系等信息进行目标的提取和分析,是当前高分辨率信息提取技术的主要方法。研究了采用面向对象目标的思想将MRF方法应用于高分辨率遥感影像的道路目标提取中,并进行了道路提取实验。     

An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.     

影像与激光雷达数据的城市地物信息提取

针对城市地物信息提取中地物边界难以确定、分类精度不高的问题,该文提出一套综合利用影像及激光雷达点云高程信息的面向对象分类方法。在分割中,各类地物的最佳分割尺度由监督法分割精度评价确定,最终分割结果利用粒度理论下的分割尺度综合方法进行合成,能兼顾不同地物最优分割尺度,获得准确地物边界;在分类中,采用ReliefF特征选择算法度量从影像及点云数据提取的对象特征重要度,选择最佳特征组合,并采用多分类器组合方法进行分类,以消除Hughes现象,提高分类精度。选择德国斯图加特市两块实验区进行分类实验,结果表明:该方法有利于提高大范围城市地物精细信息提取的精度和效率,具有较高的应用价值。     

基于高分辨率遥感影像的土地利用信息提取系统的设计与实现

结合土地利用调查监测业务的需求,基于三层软件架构设计了基于高分辨率遥感影像的土地利用信息提取系统。系统在ArcGIS Engine平台上开发,采用COM技术构建功能模块。设计的功能模块包括数据管理、视图浏览、影像分割、特征选取和面向对象分类等。系统设计并实现了面向对象的多分类器组合的分类器,保证了影像分类精度。系统分类数据可直接纳入土地利用数据库,有效满足了土地利用调查监管的时效性。本系统易于维护和扩展,具有很好的应用价值。     

基于光谱变换与融合的IKONOS多光谱影像分类

利用多种光谱线性变换及其融合的方法,减少波段间的信息冗余,提高光谱特征的可分性,并用实验证明了算法的有效性。     

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery

Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (WV-2) imagery. Supervised per-pixel classification algorithms including Maximum Likelihood and Support Vector Machine (SVM) were utilized to evaluate the capability of spectral-based classifiers to classify urban features. Object-oriented classification was performed using supervised SVM and fuzzy rule-based approach to add spatial and texture attributes to spectral information. Supervised object-oriented SVM achieved 82.80% overall accuracy which was the better accuracy compared to supervised per-pixel classifiers. Classification based on the proposed fuzzy rule-based system revealed satisfactory output compared to other classification techniques with an overall accuracy of 87.10% for pervious surfaces and an overall accuracy of 85.19% for impervious surfaces.     

Classification of boreal macrotidal littoral zone habitats in the Gulf of Maine: comparison of IKONOS and CASI multispectral imagery

Increasingly, remote sensing has become a useful tool for mapping and measuring terrestrial and aquatic environments. Advances in the spatial and spectral resolution of satellite-borne sensors have allowed affordable investigations of littoral macrotidal coastal systems that previously required more costly aircraft-based imagery. In this communication, we compare the results from analysis of a 4 m spatial resolution, multispectral IKONOS satellite image of the intertidal habitats of Islesboro, Maine, USA with that of an aerial compact airborne spectral imager survey of the same regions captured 4 years earlier. There was 72% agreement between the surveys in spite of the temporal gaps between the images. Accuracy varied by habitat class and the perceived error can be assigned to temporal and definitional issues rather than basic acquisition and analytic protocols. Most of the error can be explained by: (1) inadequacy of training sites, (2) temporal variations and (3) class definitions. We conclude that IKONOS imagery provides sufficient spatial and spectral resolution to map and monitor diverse intertidal habitats as found in the macrotidal Gulf of Maine.     

面向对象的矿产地理数据库的设计与建立

传统的GIS数据模型难以表达非结构化数据和GIS中具有复杂结构的数据 ,更不能表达数据间继承、聚合等在GIS中广泛使用的关系。面向对象的技术则可以很好地解决上述问题。本文介绍了面向对象的地理数据库的设计和建立 ,并结合矿区矿产管理工作中的特殊性 ,通过实例分析了其在矿区矿产地理数据库建立中的应用。     

遥感影像目标的尺度特征卷积神经网络识别法

高分辨率遥感影像的目标检测与识别,是高分对地观测系统中影像信息自动提取及分析理解的重要内容。针对传统影像目标检测与识别算法中人工设计特征稳健性与普适性差的问题,本文提出基于高分辨率遥感影像目标尺度特征的卷积神经网络检测与识别方法。首先通过统计遥感影像目标的尺度范围,获得卷积神经网络训练与测试过程中目标感兴趣区域合适的尺度大小。然后根据目标感兴趣区域合适的尺度,提出基于高分辨率遥感影像目标尺度特征的卷积神经网络检测与识别架构。通过WHU-RSone数据集对本文卷积神经网络架构与Faster-RCNN架构对比测试验证。试验结果表明,本文架构ZF模型和本文架构VGG-16模型的mean average precision(mAP)分别比Faster-RCNNZF模型和Faster-RCNNVGG-16模型提高8.17%和8.31%,本文卷积神经网络架构可获得良好的影像目标检测与识别效果。     

Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery

Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.