Multiple Classifier Systems for Hyperspectral Remote Sensing Data Classification

One of the most widely used outputs of remote sensing technology is Hyperspectral image. This large amount of information can increase classification accuracy. But at the same time, conventional classification techniques are facing the problem of statistical estimation in high-dimensional space. Recently in remote sensing, support vector machines (SVMs) have shown very suitable performance in classifying high dimensionality problem. Another strategy that has recently been used in remote sensing is multiple classifier system (MCS). It can also improve classification accuracy by combining different classifier methods or by a diversity of the same classifier. This paper aims to classify a Hyperspectral data using the most common methods of multiple classifier systems i.e. adaboost and bagging and a MCS based on SVM. The data used in the paper is an AVIRIS data with 224 spectral bands. The final results show the high capability of SVMs and MCSs in classifying high dimensionality data.     

基于改进的模糊c-均值聚类方法遥感影像分类研究(英文)

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel （GK） and Gath-Geva （GG） algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.     

A fuzzy topology-based maximum likelihood classification

Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy.     

Classification of CBERS-2 Imagery with Fuzzy ARTMAP Classifier

A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery.     

Classification of CBERS-2 imagery with fuzzy ARTMAP classifier

A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery.     

遥感影像的全模糊监督分类

设计了一个基于神经元网络的全模糊训练、分类和精度评估方法,并成功地应用于一个城郊型土地覆盖分类。结果表明,该方法灵活、适应性强,并能取得较好的分类精度。     

基于粒子群优化的模糊特征自适应选择方法

模糊特征的选择影响着模糊分类的结果。从大量模糊特征中选择出有效特征进行分类,存在着一定的难度。粒子群优化算法(PSO)是基于群体智能的新型进化计算技术,具有自适应、自组织等智能特性,具有强大的寻找最优解的能力。将离散二进制PSO用于模糊特征选择,实现了基于PSO的模糊特征自适应选择方法,并通过航空和卫星遥感影像的模糊分类实验,验证了此方法的有效性。     

用模糊ARTMAP算法对CBERS-2数据进行分类

用模糊ARTMAP（fuzzy adaptive resonance theorymap）神经网络算法对CBERS-2数据进行了分类实践。首先介绍了模糊ARTMAP神经网络的算法原理和具体训练分类过程；然后用2004年9月新疆石河子地区的影像数据进行土地利用分类试验，并将分类结果与基于统计的最大似然法（MLC）、反向传播神经网络（BP）的分类结果作比较，总分类精度比MLC和BP算法分别提高9．9％和4．6％。结果表明，模糊ARTMAP对试验区CBERS-2影像上的裸地识别能力很强，对高分辨率的CBERS-2影像可获得很好的分类结果。     

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.     

自适应模糊规则分类方法及在TM土地覆盖分类中的应用研究

根据自组织网络和模糊逻辑推理,实现土地覆盖自适应模糊规则分类方法。该方法通过网络的节点和权值提取出模糊规则,调整网络中节点个数(即相应增加规则节点数)和权值向量,使模糊规则自动生成,并利用模糊逻辑推理,完成TM土地覆盖分类。对拒分类的像元,自适应增加K值使其可分。该方法所得分类精度及Kapp系数与最大似然分类方法结果相比分别提高了2.7%和2.9%;与自组织网络相比,总精度相差不大,而Kapp系数低1%。实验证明,如何提取和表示非光谱知识,从而解决类别混淆等问题,是提高自适应模糊规则分类性能的关键     

模糊分类技术在作物类型识别中的应用

介绍了模糊分类技术,并将其应用于多时相ScanSAR的作物识别中。模糊分类技术比传统的最大似然法具有较高的识别精度。结合雷达图像的自身特点,将模糊分类技术与上下文处理相结合,是雷达图像处理的一种有效途径     

城市地图数据库面实体匹配技术

同一地区不同来源地图数据库同名面实体的识别或匹配对空间数据库的集成与信息共享非常有意义。该文研究了城市地图数据库同名面实体匹配的有关问题 ,提出了基于模糊拓扑关系分类的面实体匹配方法 ,该方法充分考虑了源地图数据库的不确定性 ,能处理非一对一的匹配情况。匹配结果不仅可以作为城市地图数据库集成和信息共享的依据 ,还可以用来分析两个地图数据库之间的差异 ,进行变化检测与自动更新     

Classification of remote sensing images from urban areas using a fuzzy possibilistic model

The classification of very high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest of exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile (DMP) obtained with a granulometric approach, using opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this letter, we present an interpretation of the DMP in terms of a fuzzy measurement of the characteristic size and contrast of each structure. This fuzzy measure can be compared to predefined possibility distributions to derive a membership degree for a set of given classes. The decision is taken by selecting the class with the highest membership degree. This model is illustrated and validated in a classification problem using IKONOS images.     

挖掘模糊空间关联规则的方法

将模糊关联规则挖掘方法与模糊空间概念层次表达、模糊空间关系层次分析等结合起来，研究模糊空间关联规则挖掘的理论和方法。对于挖掘算法以及规则的置信度和隶属度计算问题，文中结合应用实际，给出了详细理论推演和算法实现过程。     

一种新的高分辨率遥感影像模糊监督分类方法

针对高分辨率遥感影像分类中由于细节特征突出、同质区域光谱测度变异性增大所带来的像素类属的不确定性及模型的不确定性等造成的误分结果,提出一种基于模糊隶属函数的监督分类方法。对同质区域定义高斯隶属函数模型用来表征像素类属不确定性;模糊化该隶属函数参数建立影像模糊隶属函数,以建模同质区域光谱测度的不确定性;用训练样本在所有类别中的模糊隶属函数及原隶属函数（高斯隶属函数）中的隶属度为输入,建立模糊线性神经网络模型作为目标函数,实现分类决策。该算法和经典算法对World View-2全色合成影像及真实影像进行定性和定量分类实验,分类结果验证了文中方法具有更高的分类精度。     

自适应距离和模糊拓扑优化的模糊聚类SAR影像变化检测

针对模糊聚类算法的不足,结合差分影像的特点,提出一种基于自适应距离（adaptive distance）和模糊拓扑（fuzzy topology）理论的SAR影像变化检测技术框架（FATCD）。FATCD首先基于自适应距离公式提出一种自适应的样本到聚类中心的距离计算方法,优化了聚类过程中像元隶属度的计算公式,提高了模糊隶属度函数的准确程度;而后利用模糊拓扑理论改进传统去模糊化方式最大隶属度原则,从而增强了去模糊化过程。借助这两点,FATCD提高了模糊聚类变化检测的性能。两组真实SAR影像数据的试验结果表明本文方法可行、有效。     

一种模糊聚类的遥感影像分析方法研究

针对传统模糊聚类的遥感影像分析方法的不足,重点研究基于模糊ISODATA聚类的遥感影像分析。通过Matlab软件编程实现基于迭代自组织数据分析技术、模糊C均值聚类、模糊ISODATA算法对合成图像、纹理图像及真实遥感影像的分类,并对其分类结果进行讨论。通过实验数据对比,评价FISODATA算法的优越性。实验结果表明:ISODATA算法及FISODATA算法都能够实现变类,而FCM算法只能在固定聚类数下进行分类,但是,ISODATA算法分类机制不稳定,不能每次都确定正确聚类数。在迭代过程中,将FISODATA算法引入模糊集理论,便能够快速准确的实现聚类数的确定。     

二型模糊集合在遥感影像土地利用分类中的应用

在遥感影像土地利用分类中,传统的布尔分类法只考虑地物光谱信息,用"是"与"非"的二值确定逻辑状态分类影像,当存在较多混合像元时,其不能满足一些实际应用的要求。模糊分类法综合了地物光谱特征、空间特征和对象的拓扑关系,对每个像元的归属用[0～1]的模糊度来表示,有效地解决了该问题。通过对研究区TM影像进行传统布尔法分类和模糊分类法分类的结果比较可知:对混合像元较多且波段较少的遥感影像土地利用分类,二型模糊集合分类能够适应用户不同精度需求,并能交互式输出结果。     

An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods.