模糊ARTMAP神经网络在TM影像分类中的应用

阐述了模糊ARTMAP网络结构及其采用的算法，提出一种引入遥感图像判读结果的警戒系数自动调整算法，能够解决人为选择警戒参数效率低、难以取得合适数值的问题．仿真结果表明，具有警戒系数调整功能的模糊ARTMAP神经网络能够有效地对向海自然保护区的TM影像进行分类，它与最大似然法和传统的模糊ARTMAP神经网络相比，对样本的依赖程度较低，分类精度较高．     

基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价

主要讨论了基于Fuzzy ARTMAP神经网络的高分辨率遥感图象土地覆盖分类方法及其实践．首先介绍了Fuzzy ARTMAP神经网络的原理，然后用SPOT XS图象试验数据进行土地覆盖分类．分类结果与传统的最大似然监督分类(MLC)、反馈式(Back Propagation，BP)神经网络的分类结果进行了比较．通过抽取500个样点对3种分类结果进行精度评价表明，Fuzzy ARTMAP神经网络相对其他两种方法，分类精度均有不同程度的改善，具有更好的分类结果，总分类精度比MLC和BP算法分别提高17．41％、7．32％．最后，对不同分类方法对于土地覆盖分类结果的影响进行了评价和分析．试验表明，Fuzzy ARTMAP神经网络用于高分辨图象土地覆盖分类研究可以获得相对较好的分类结果．     

FUZZY ARTMAP在三字词声调识别中的应用

三字词音节声调模式具有连续语音中音节声调模式的特征，声调的提取和识别远较孤立字困难。采用小波变换方法提取语音基音，用Fuzzy ARTMAP神经网络进行声调识别，获得了比BP网络更好的实验结果。分析了仿真参数对识别结果的影响，讨论了Fuzzy ARTMAP神经网络中的过拟合问题，给出了一种基于Fuzzy ARTMAP神经网络的三字词声调识别方法。     

基于一维Kohonen网络的遥感影像分类方法研究

分析了Kohonen网络的训练模式和聚类特性,选用规模相对较小的一维Kohonen网络,并调整网络输出层的规模和邻域形状,优化网络结构;同时根据多光谱遥感影像中地物波谱曲线特征,通过不同波段组合、波段权重系数调整等方法对输入数据进行预处理,使该方法更适用于多光谱遥感影像分类和专题提取.本文以浙江省绍兴地区多光谱遥感影像分类为例,研究结果表明使用改进后的分类方法可以有效提高分类精度.     

利用概率主题模型的遥感影像半监督分类

土地覆盖是自然环境与人类活动相互作用的中心,而土地覆盖信息主要是通过遥感影像分类来获取,因此影像分类是遥感影像分析的最基本问题之一。在参考基于概率主题模型的高分辨率遥感影像聚类分析的基础上,通过半监督学习最典型的生成模型方法引出了基于概率主题模型的半监督分类(SS-LDA)算法。借鉴SS-LDA模型在文本识别应用的流程,构建了基于SS-LDA算法的高分辨率遥感影像分类的基本流程。通过实验证明,相对于传统的非监督分类与监督分类算法,SS-LDA算法能够获取较高精度的影像分类结果。     

基于粒子群训练的人工神经网络应用于多光谱遥感影像分类研究

为了提高多光谱遥感影像的分类精度,提出一种基于粒子群训练的人工神经网络的多光谱遥感影像的分类方法。该方法先建立一个针对多光谱遥感影像的神经网络分类模型,然后引入粒子群算法对神经网络进行网络权值与阈值的优化,再利用训练好的神经网络对多光谱遥感影像进行分类。该方法不仅利用了人工神经网络在解决多光谱遥感影像混合光谱的优势,而且克服了BP神经网络在训练时候收敛速度过慢、振荡的缺点。实验结果证明：基于粒子群训练的人工神经网络方法能够比较好地提高多光谱遥感影像的分类精度。     

基于粒子群训练的人工神经网络应用于多光遥感影像分类研究

为了提高多光谱遥感影像的分类精度,提出一种基于粒子群训练的人工神经网络的多光谱遥感影像的分类方法.该方法先建立一个针对多光谱遥感影像的神经网络分类模型,然后引入粒子群算法对神经网络进行网络权值与阈值的优化,再利用训练好的神经网络对多光谱遥感影像进行分类.该方法不仅利用了人工神经网络在解决多光谱遥感影像混合光谱的优势,而且克服了BP神经网络在训练时候收敛速度过慢、振荡的缺点.实验结果证明:基于粒子群训练的人工神经网络方法能够比较好地提高多光谱遥感影像的分类精度.     

RBF神经网络在遥感影像分类中的应用研究

用RBF神经网络进行遥感影像分类，在网络结构设计上使RBF层与输出层的节点数都等于所要分类的类别数。用Kohonen聚类算法确定RBF中心的时候，用训练样本的均值作为初始中心，并在RBF宽度进行求取的时候进行了改进，以避免内存溢出。所设计的RBF神经网络分类模型具有结构简单、算法简洁的优点。实验结果表明，该方法用于遥感影像分类取得了较高的分类精度，具有实际应用价值。     

AGA-BP模型在遥感影像分类中的应用研究

作为遥感研究的关键技术,遥感影像分类一直是遥感研究热点;针对目前采用BP神经网络模型进行遥感影像分类时存在的对初始权阈值敏感、易陷入局部极值和收敛速度慢的问题,为了提高BP模型遥感影像分类精度,将自适应遗传算法引入到BP网络模型参数选择中;首先运用自适应遗传算法对BP模型权阈值参数进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于自适应遗传算法的BP网络分类模型,并将其应用到遥感影像数据分类研究中;仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值。     

基于改进型模糊ARTMAP网络的CBR遥感图像分类系统研究

针对难以及时地获取充足而准确的遥感样本、缺乏积累和管理遥感样本的有效手段是制约遥感图像分类技术发展的瓶颈问题。构建了基于改进型模糊ARTMAP网络的CBR(case-based reasoning范例推理)遥感图像分类系统。系统将改进型模糊ARTMAP网络作为范例的知识提取器和图像分类器,运用CBR求解策略实现遥感样本知识的合理储备、优化组合和重复利用。分别应用本文所建系统、最大似然法、BP网络和改进型模糊ARTMAP网络对向海自然保护区TM遥感图像进行分类操作,实验结果表明,本文建立的系统与其他分类方法相比,能够更好地提高遥感样本数据的利用效率和遥感图像的分类精度,而且一定程度上解决了在样本有限的条件下如何高效利用已有数据进行遥感图像分类的问题。     

Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing

Mixture analysis is a necessary component for capturing sub-pixel heterogeneity in the characterization of land cover from remotely sensed images. Mixture analysis approaches in remote sensing vary from conventional linear mixture models to nonlinear neural network mixture models. Linear mixture models are fairly simple and generally result in poor mixture analysis accuracy. Neural network models can achieve much higher accuracy, but typically lack interpretability. In this paper we present a mixture discriminant analysis (MDA) model for inferring land cover fractions within forest stands from Landsat Thematic Mapper images. Specifically, individual class distributions are modeled as mixtures of subclasses of Gaussian distributions, and land cover fractions are estimated using the corresponding posterior probabilities. Compared to a benchmark study on accuracy of mixture models with Plumas National Forest data, this MDA model easily outperforms traditional linear mixture models and parallels the performance of the ARTMAP neural network mixture model. In other words, the MDA model is observed to successfully combine the performance characteristics of more complex neural network models (due to the nonlinear nature of its classification rules), with the ease of interpretation associated with linear mixture models (due to its relatively simple structure). MDA models therefore offer an attractive alternative for addressing the mixture modeling problem in remote sensing.     

Pattern recognition for sensor array signals using Fuzzy ARTMAP

A Fuzzy ARTMAP classifier for pattern recognition in chemical sensor array was developed based on Fuzzy Set Theory and Adaptive Resonance Theory. In contrast to most current classifiers with difficulty in detecting new analytes, the Fuzzy ARTMAP system can identify untrained analytes with comparatively high probability. And to detect presence of new analyte, the Fuzzy ARTMAP classifier does not need retraining process that is necessary for most traditional neural network classifiers. In this study, principal component analysis (PCA) was first implemented for feature extraction purpose, followed by pattern recognition using Fuzzy ARTMAP classifiers. To construct the classifier with high recognition rate, parameter sensitive analysis was applied to find critical factors and Pareto optimization was used to locate the optimum parameter setting for the classifier. The test result shows that the proposed method can not only maintain satisfactory correct classification rate for trained analytes, but also be able to detect untrained analytes at a high recognition rate. Also the Pareto optimal values of the most important parameter have been identified, which could help constructing Fuzzy ARTMAP classifiers with good classification performance in future application.     

FasART模糊神经网络用于遥感图象监督分类的研究

说明了遥感图象数据的非线性性质，目视的图象分类实践是一个模糊推理的过程，模糊神经网络遥感图象分类符合其事物的内在规律，具有理论优势，分析了模糊ART，模糊ARTMAP和FasART模型的结构和原理，详细地阐述了FasART是一种基于模糊逻辑系统的神经网络，提出了一种简化的FasART模型，改变了一般遥感数据的模糊化方法，采用中巴资源一号卫星数据进行测试实验，结果表明，该简化的FasART模型能用于遥感图象的监督分类，其分类精度高于模糊ARTMAP神经网络和K均值算法，且性能稳定，有较好的抗干扰能力，尤其具有良好的处理两组相似程度比较接近的，和同组数据模式变化较大的非线性数据的能力。     

Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network

Mixed pixels are a major problem in mapping land cover from remotely sensed imagery. Unfortunately, such imagery may be dominated by mixed pixels, and the conventional hard image classification techniques used in mapping applications are unable to appropriately represent the land cover of mixed pixels. Fuzzy classification techniques can, however, accommodate the partial and multiple class membership of mixed pixels, and be used to derive an appropriate land cover representation. This is, however, only a partial solution to the mixed pixel problem in supervised image classification. It must be reognised that the land cover on the ground is fuzzy, at the scale of the pixel, and so it is inappropriate to use procedures designed for hard data in the training and testing stages of the classification. Here an approach for land cover classification in which fuzziness is accommodated in all three stages of a supervised classification is presented. Attention focuses on the classification of airborne thematic mapper data with an artificial neural network. Mixed pixels could be accommodated in training the artificial neural network, since the desired output for each training pixel can be specified. A fuzzy land cover representation was derived by outputting the activation level of the network's output units. The activation level of each output unit was significantly correlated with the proportion of the area represented by a pixel which was covered with the class associated with the unit (r>0.88, significant at the 99% level of confidence). Finally, the distance between the fuzzy land cover classification derived from the artificial neural network and the fuzzy ground data was used to illustrate the accuracy of the land cover representation derived. The dangers of hardening the classification output and ground data sets to enable a conventional assessment of classification accuracy are also illustrated; the hardened data sets were over three times more distant from each other than the fuzzy data sets.     

Feature selection and land cover classification of a MODIS-like data set for a semiarid environment

The advent of the Earth Observing System (EOS), and the Moderateresolution Imaging Spectroradiometer (MODIS) in particular, will usher in a new era of global remote sensing by providing very large data volumes for interpretation and processing. Since many data streams will contain correlated data, feature selection is an important practical problem for such activities as classification of global land cover based on spectral, temporal, spatial and directional data. Treebased classification methods offer a suite of promising approaches to extraction of meaningful features from large measurement spaces. This research develops a tree-based model that performs feature selection on a satellite database containing information on land covers in a semiarid region in Cochise County, Arizona. In addition, we test the abilities of several classifiers to correctly label land cover using this reduced set of inputs under various sampling schemes. Results from this analysis indicate that decision trees can reduce a high-dimension dataset to a manageable set of inputs that retain most of the information of the original database, while remaining largely insensitive to choice of sampling strategy, and that Fuzzy ARTMAP, a type of artificial neural network classifier, achieves highest accuracy in comparison to maximum-likelihood or decision-tree classifiers.     

GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm

Fuzzy ARTMAP (FAM), which is a supervised model from the adaptive resonance theory (ART) neural network family, is one of the conspicuous neural network classifier. The generalization/performance of FAM is affected by two important factors which are network parameters and presentation order of training data. In this paper we introduce a genetic algorithm to find a better presentation order of training data for FAM. The proposed method which is the combination of genetic algorithm with Fuzzy ARTMAP is called Genetic Ordered Fuzzy ARTMAP (GOFAM). To illustrate the effectiveness of GOFAM, several standard datasets from UCI repository of machine learning databases are experimented. The results are analyzed and compared with those from FAM and Ordered FAM which is used to determine a fixed order of training pattern presentation to FAM. Experimental results demonstrate the performance of GOFAM is much better than performance of Fuzzy ARTMAP and Ordered Fuzzy ARTMAP. In term of network size, GOFAM performs significantly better than FAM and Ordered FAM.     

An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant

The main objective of this paper is to investigate the use of Quality Threshold ARTMAP (QTAM) neural network in classifying the feature vectors generated by moment invariant for the insect recognition task. In this work, six different types of moment invariant technique are adopted to extract the shape features of the insect images. These moment techniques are Geometrical Moment Invariant (GMI), United Moment Invariant (UMI), Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI), Tchebichef Moment Invariant (TMI) and Krawtchouk Moment Invariant (KMI). All the moment techniques are analyzed using the concept of intraclass and interclass analysis. In intraclass analysis, several computation methods are introduced in order to examine the invariance properties of adopted moment techniques for the same insect object. Meanwhile, the classification accuracy of neural networks is adopted to measure the interclass characteristic and the effectiveness of moment technique in extracting the shape features of insect images. Other types of neural networks are also utilized in this research work. This includes novel enhancement technique based on the Gaussian and Mahalanobis function that design to increase its prediction accuracy. All the other networks used to classify the feature vectors are based on the Fuzzy ARTMAP (FAM) neural network. The experimental results indicated that the Krawtchouk Moment Invariant technique generated the highest classification accuracy for most of the networks used and generated the smallest error for the intraclass analysis. Using different normalization technique, the Quality Threshold ARTMAP and Mahalanobis distance function (QTAM-m) network gave the highest insect recognition results when compared to other networks.