Earth observational research using multistage EOS-like data

This grant is funded as a part of a program in which both research and educational impact are intended. Research work under this grant is directed at the understanding and use of future hyperspectral data such as that from imaging spectrometers. Specifically, the objectives of the work are (1) to prepare suitable means for analyzing data from sensors which have large numbers of spectral bands, (2) to advance the fundamental understanding of the manner in which soils and vegetative materials reflect high spectral resolution optical wavelength radiation, and (3) to maximize the impact of the results on the educational community. Over the life of the grant, the work has thus involved basic Earth science research and information system technique understanding and development in a mutually supportive way, however, more recently it has become necessary to focus the work primarily on areas (1) and (3). During the last year, the level of effort on this grant has been reduced to half its previous value. We have also been advised that this grant will end with the current year, thus this will be the penultimate semiannual progress summary

Classification of hyperspectral imagery with neural networks: comparison to conventional tools

Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data because the complexity of such data challenges the limitations of many conventional methods. Artificial neural networks (ANNs) were shown to outperform traditional classifiers in many situations. However, studies that use the full spectral dimensionality of hyperspectral images to classify a large number of surface covers are scarce if non-existent. We advocate the need for methods that can handle the full dimensionality and a large number of classes to retain the discovery potential and the ability to discriminate classes with subtle spectral differences. We demonstrate that such a method exists in the family of ANNs. We compare the maximum likelihood, Mahalonobis distance, minimum distance, spectral angle mapper, and a hybrid ANN classifier for real hyperspectral AVIRIS data, using the full spectral resolution to map 23 cover types and using a small training set. Rigorous evaluation of the classification accuracies shows that the ANN outperforms the other methods and achieves ?90% accuracy on test data

Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure

Factors influencing the accuracy of remote sensing classifications: a comparative study

Within last 20 years, a number of methods have been employed for classifying remote sensing data, including parametric methods (e.g. the maximum likelihood classifier) and non-parametric classifiers (such as neural network classifiers).Each of these classification algorithms has some specific problems which limits its use. This research studies some alternative classification methods for land cover classification and compares their performance with the well established classification methods. The areas selected for this study are located near Littleport (Ely), in East Anglia, UK and in La Mancha region of Spain. Images in the optical bands of the Landsat ETM+ for year 2000 and InSAR data from May to September of 1996 for UK area, DAIS hyperspectral data and Landsat ETM+ for year 2000 for Spain area are used for this study. In addition, field data for the year 1996 were collected from farmers and for year 2000 were collected by field visits to both areas in the UK and Spain to generate the ground reference data set. The research was carried out in three main stages.The overall aim of this study is to assess the relative performance of four approaches to classification in remote sensing - the maximum likelihood, artificial neural net, decision tree and support vector machine methods and to examine factors which affect their performance in term of overall classification accuracy. Firstly, this research studies the behaviour of decision tree and support vector machine classifiers for land cover classification using ETM+ (UK) data. This stage discusses some factors affecting classification accuracy of a decision tree classifier, and also compares the performance of the decision tree with that of the maximum likelihood and neural network classifiers. The use of SVM requires the user to set the values of some parameters, such as type of kernel, kernel parameters, and multi-class methods as these parameters can significantly affect the accuracy of the resulting classification. This stage involves studying the effects of varying the various user defined parameters and noting their effect on classification accuracy. It is concluded that SVM perform far better than decision tree, maximum likelihood and neural network classifiers for this type of study. The second stage involves applying the decision tree, maximum likelihood and neural network classifiers to InSAR coherence and intensity data and evaluating the utility of this type of data for land cover classification studies. Finally, the last stage involves studying the response of SVMs, decision trees, maximum likelihood and neural classifier to different training data sizes, number of features, sampling plan, and the scale of the data used. The conclusion from the experiments presented in this stage is that the SVMs are unaffected by the Hughes phenomenon, and perform far better than the other classifiers in all cases. The performance of decision tree classifier based feature selection is found to be quite good in comparison with MNF transform. This study indicates that good classification performance depends on various parameters such as data type, scale of data, training sample size and type of classification method employed

Factors influencing the accuracy of remote sensing classifications: a comparative study

Within last 20 years, a number of methods have been employed for classifying remote sensing data, including parametric methods (e.g. the maximum likelihood classifier) and non-parametric classifiers (such as neural network classifiers).Each of these classification algorithms has some specific problems which limits its use. This research studies some alternative classification methods for land cover classification and compares their performance with the well established classification methods. The areas selected for this study are located near Littleport (Ely), in East Anglia, UK and in La Mancha region of Spain. Images in the optical bands of the Landsat ETM+ for year 2000 and InSAR data from May to September of 1996 for UK area, DAIS hyperspectral data and Landsat ETM+ for year 2000 for Spain area are used for this study. In addition, field data for the year 1996 were collected from farmers and for year 2000 were collected by field visits to both areas in the UK and Spain to generate the ground reference data set. The research was carried out in three main stages.The overall aim of this study is to assess the relative performance of four approaches to classification in remote sensing - the maximum likelihood, artificial neural net, decision tree and support vector machine methods and to examine factors which affect their performance in term of overall classification accuracy. Firstly, this research studies the behaviour of decision tree and support vector machine classifiers for land cover classification using ETM+ (UK) data. This stage discusses some factors affecting classification accuracy of a decision tree classifier, and also compares the performance of the decision tree with that of the maximum likelihood and neural network classifiers. The use of SVM requires the user to set the values of some parameters, such as type of kernel, kernel parameters, and multi-class methods as these parameters can significantly affect the accuracy of the resulting classification. This stage involves studying the effects of varying the various user defined parameters and noting their effect on classification accuracy. It is concluded that SVM perform far better than decision tree, maximum likelihood and neural network classifiers for this type of study. The second stage involves applying the decision tree, maximum likelihood and neural network classifiers to InSAR coherence and intensity data and evaluating the utility of this type of data for land cover classification studies. Finally, the last stage involves studying the response of SVMs, decision trees, maximum likelihood and neural classifier to different training data sizes, number of features, sampling plan, and the scale of the data used. The conclusion from the experiments presented in this stage is that the SVMs are unaffected by the Hughes phenomenon, and perform far better than the other classifiers in all cases. The performance of decision tree classifier based feature selection is found to be quite good in comparison with MNF transform. This study indicates that good classification performance depends on various parameters such as data type, scale of data, training sample size and type of classification method employed