基于空间数据发掘的遥感图象分类方法研究

采用数据发掘技术从ＧＩＳ数据库和遥感图像中发现知识,用于改善遥感图像分类。提出了两种实施空间数据归纳学习的途径;在空间对象粒度上学习直接在像元粒度上学习。分析了两种粒度学习的特点和适用范围,同时提出了一种归纳学习与传统图像分类法的结合方式。用北京地区ＳＰＯＴ多光谱图像和ＧＩＳ数据库进行土地利用分类的试验证明,归纳学习能较好地解决同划物、同物异谱等问题,显提高分类精度,并且能够根据发现的知识进一步     

遥感图像在土地利用更新中的应用研究：以里下河地区防汛信息系统（ …

以里下河地区防汛信息系统为例,阐述了利用ＴＭ遥感图像进行ＧＩＳ土地利用更新的试验研究。采用了最大似然分类和模糊分类两种监督分类方法进行分类试验。提出了两种提高训练区采样效率的基于ＧＩＳ本底数据的采样方法,即半人工采样方法和全自动分类区直方图提取法。对上述方法分别做了土地利用分类试验,对分类结果进行了分析比较,并作出了相应的结论。     

GIS数据库的遥感更新方法研究

本文从空间实体和数值库元素的相似一致性出发,将更新定义为一个动态优化过程。围绕空间单元的识别与更新,研究和讨论图像特征增强,ＧＩＳ数据库信息,遥感地学分析在更新中的应用。最后采用ＴＭ及ＳＯＰＴ遥感图像对ＧＩＳ数据库进行了更新试验。     

遥感图像面状目标识别的智能可视化模型

建立实用的遥感图像分析系统涉及诸多因素，其中，形成有效的目标提取算法体系和设立适于目前人工智能水平的基于知识的框架是两个核心问题。本文提出了一种利用对光谱特征空间分布进行可视化图形处理的新的分类方案，继而以光谱、纹理和空间知识对分类结果进行优化。这种集可视化分析、栅格ＧＩＳ处理功能和人类判读专家知识于一体的系统为解决遥感图像地物识别这一复杂问题提供了有效途径。     

基于遥感数据的GIS数据库几何精度分析

通过用误差传播理论及ＧＩＳ数据库与遥感资料的叠合两种方法,对海宁市土地详查ＧＩＳ数据库进行几何精度分析以说明官两种方法的可行性。     

一种基于知识的遥感图像模糊分类方法

本文提出了一种基于知识的遥感图像模糊分类方法，在传统的模糊分类方法中加入了从GIS数据库中发现的知识，用它来辅助进行遥感图像分类，实验结果表明，分类的精度与传统的模糊分类方法相比有了较大程度的提高，是一种较好的遥感图像分类方法。     

Rough集理论及其在GIS属性分析和知识发现中的应用

介绍了Ｒｏｕｇｈ集理论的概念与方法,并将共全面引入ＧＩＳ领域,归纳整理出Ｒｏｕｇｈ集理论用于ＧＩＳ中属性分析知识发现的一整套方法,为ＧＩＳ的属性分析和知识发现开辟了一条新途径。     

通用型遥感图像理解专家系统的研究

论述了研制通用型遥感图像理解专家系统的基本原理，较详尽地讨论了这种专家系统事实库的建立、知识表达和知识获取的方法、模糊推理的方法及与遥感图像处理系统和ＧＩＳ软件系统的衔接等问题，介绍了具体的通用型专家系统ＲＳＳＴＡＲ的实现及功能。     

将GIS数据直接纳入图像处理

探讨了ＲＳ与ＧＩＳ在空间数据处理过程中各种可能的结合方式,明确了目前ＲＳ与ＧＩＳ集成中的一个重要任务就是将ＧＩＳ数据直接纳入图像处理,同时提出,解决这个问题应先从空间数据处理的粒度入手,即寻找ＲＳ与ＧＩＳ共同的处理单元,给出了将ＧＩＳ数据直接纳入图像处理的实例,说明在行程线这个粒度上已能将ＲＳ与ＧＩＳ很好的结合在一起了。     

基于GOODALL相近指数的遥感图像和其它空间数据综合分类方法

介绍 Ｄａｖｉｄ Ｗ． Ｇｏｏｄａｌｌ 的基于概率的相近指数理论,研究它被应用在遥感图像和其它空间数据综合分类中的可能性,并首次在 Ｇ Ｒ Ａ Ｓ Ｓ环境下实现了基于 Ｄａｖｉｄ Ｗ． Ｇｏｏｄａｌｌ 的相近指数的遥感图像和其它空间数据综合分类算法,并对该算法进行了测试,将分类结果与其它几种较流行的分类方法结果进行了比较。     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning. Comparing with the results produced only by Bayes classification, the overall accuracy increased by 11% and the accuracy of some classes, such as garden and forest, increased by about 30%. The results indicate that inductive learning can resolve spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdividing some classes with the discovered knowledge.     

GIS辅助下的Bayes法遥感影像分类

介绍了Bayes分类器 ,提出了从GIS空间数据库中挖掘知识用以辅助进行遥感影像分类的方法。文中以规则的形式表示遥感影像的解译知识 ,并使用其它地理辅助数据 ,从遥感影像处理、地理辅助数据、专家知识一体化的角度出发 ,使用基于知识的方法进行了分类研究 ,改善了分类精度。实验表明这是一种较好的分类方法。     

地理信息系统支持下的知识获取及其在遥感影像植被分类中的应用研究

本文根据植被类型分布与地理环境因子的关系,在地理信息系统和遥感技术支持下,通过ＧＩＳ叠加、统计分析操作,建立植被分布与年积温、降水量、海拔高度、土壤类型等环境因子的定量化知识向量表。综合应用所得到的地学知识向量表和植被光谱特征值进行分类试验,得到研究区的植被分布图。文章以贺兰山地区为例,详细介绍该方法的应用。     

基于分类规则挖掘的遥感影像分类研究

分析了目前遥感影像的统计分类、神经网络分类及基于符号知识的逻辑推理分类方法的优缺点.以GIS为平台,构建了多源空间数据库,将数据挖掘的思想和方法引入遥感影像分类中,提出了面向分类规则挖掘的遥感影像分类框架.针对遥感光谱数据及其他空间数据的特点,定义了连续属性样本分类概念和分割点评价指标,提出了一种新的连续属性样本分类规则挖掘算法.选择一个试验区,采用该算法分别对遥感光谱数据、遥感光谱和DEM数据相结合的数据进行分类规则挖掘、遥感影像分类和分类精度比较.结果表明：（1）该算法具有较高的分类精度;（2）加入DEM等与分类相关的其他空间数据可以提高遥感影像的分类精度.通过挖掘分类规则进行遥感影像分类,扩展了基于知识的逻辑推理分类方法中知识获取渠道,提高了分类规则获取的智能化程度.新的连续属性样本分类规则挖掘算法,扩展了归纳学习算法对连续属性样本分类的适应性.     

领域知识辅助下基于多尺度与主方向纹理的遥感影像土地利用分类

基于灰度共生矩阵(GLCM)的纹理特征在影像空间分析中具有重要作用,提出了一种在领域空间知识辅助下构建GLCM多尺度窗口与主方向权值的方法,从而提高纹理特征的有效性,并解决影像土地利用分类中存在的不确定性问题。为此,根据人类目视解译的特点,对GIS与RS数据进行集成计算:首先,在图像配准的基础上,利用经典的GIS空间数据挖掘算法,渐近式地提取领域形态知识;接着,采用关联分析法建立其与GLCM构造因子之间的响应机制,并设计了基于地类形状指数的多尺度窗口建立算法,以及基于地类主方向分布指数的方向权值测度算法。试验结果表明,领域形态知识与GLCM空间因子之间具有强相关关系,该方法提取出的纹理特征可以描述复杂地物的空间意义,算法复杂度低,性能优越,有效提高了影像土地利用分类的精度。     

特征提取策略对高分辨率遥感图像场景分类性能影响的评估

高分辨率遥感图像场景分类方法主要涉及两个环节:特征提取以及特征分类,分类器的设计已经相对成熟,当前工作的重点是特征提取策略的研究。为了进一步推动特征提取策略的研究,将特征提取策略对高分辨率遥感图像场景分类性能的影响进行了定性和定量评估。首先,回顾了高分辨率遥感图像场景分类的发展历程;然后,对现有高分辨率遥感图像场景分类方法的特征提取策略进行分类总结,并从理论上将各类特征提取策略对场景分类性能的影响进行定性评估;最后,在3个规模较大的数据集上对多种特征提取策略进行实验对比,将不同特征提取策略对场景分类性能的影响和各数据集的复杂度进行定量评估。     

A new Likelihood Ratio for supervised classification of fully polarimetric SAR data: An application for sea ice type mapping

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data. The derived Bayes risk function is based on the complex Wishart distribution. Furthermore, a new spatial criterion is incorporated with the LR classification process to produce more homogeneous classes. The application for Arctic sea ice mapping shows that the LR and the proposed spatial criterion are able to provide promising classification results. Comparison with classification results based on the Wishart classifier, the Wishart Likelihood Ratio Test Statistic (WLRTS) proposed by Conradsen et al. (2003) and the Expectation Maximization with Probabilistic Label Relaxation (EMPLR) algorithm are presented. High overall classification accuracy of selected study areas which reaches 97.8% using the LR is obtained. Combining the derived spatial criterion with the LR can improve the overall classification accuracy to reach 99.9%. In this study, fully polarimetric C-band RADARSAT-2 data collected over Franklin Bay, Canadian Arctic, is used.     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

Applications of remote sensing and geographic information systems for urban land-cover change studies in Mongolia

The aim of this study is to compare the changes that occurred in the main urban land-cover classes of Ulaanbaatar city, Mongolia, during a centralized economy with those that occurred during a market economy and to describe the socio-economic reasons for the changes. For this purpose, multi-temporal remote sensing and geographical information system (GIS) data sets, as well as census data, are used. To extract the reliable urban land-cover information from the selected remotely sensed data sets, a refined parametric classification algorithm that uses spatial thresholds defined from local and contextual knowledge is constructed. Before applying the classification decision rule, some image fusion techniques are applied to the selected remotely sensed data sets to define the most efficient fusion method for training sample selection and for defining local and contextual knowledge. Overall, the study indicates that during the centralized economy significant changes occurred in a ger area of the city, whereas during the market economy the changes occurred in all areas.