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Abstract: This study employed data mining techniques to analyze topographic and vegetative factors for the verification of landslides induced by heavy rainfall in a regional scale in Taiwan. Decision Tree and Bayesian Network data mining algorithms were implemented to extract knowledge from supplied landslide factors. Eleven topographic and vegetative factors were considered for landslide analysis. In addition to individual factors derived from digital terrain model and satellite images, combined factors were also generated from data fusion. Landslide data of the study site collected between 2004 and 2007 were used to generate rules with data mining and to construct models of landslide factors. The constructed landslide factor models were used to verify landslide detections and to predict potential landslides. The prediction results of landslide events in 2008 were then verified against field-collected ground truth to evaluate the effectiveness of the models. In this study, topographic and vegetative parameters have been proven to be significant factors for landslides in the study site. Preliminary experimental results also indicate that the constructed models with data mining can achieve high accuracy in landslide detection. However, when directly applying the models for the prediction of potential landslides, the results were not reliable due to spatial uncertainties of the data. To address this issue, a statistics-based mechanism was developed to reduce data uncertainties. The results demonstrate that after reducing data uncertainties, the models can produce more reliable results of landslide prediction in the study site, as the kappa coefficients in the prediction were substantially increased by 29% using Decision Tree and by 20% using Bayesian Network algorithms, respectively. [Copyright &y& Elsevier]

This paper presents a novel approach for the feature extraction of hyperspectral image cubes. In this paper, hyperspectral image cubes are treated as volumetric data sets. Features that are most helpful in separating different targets are effectively extracted from the hyperspectral image cubes using a newly developed high-order texture analysis method. The traditional texture measure of the gray-level cooccurrence matrix is extended to a 3-D tensor field to explore the complicated volumetric data more effectively and to extract discriminant features for better classification. As the kernel size is one of the most important parameters in statistics-based texture analysis, a semivariance analysis and a spectral separability measure are used to determine the most appropriate kernel size in the spatial and spectral domains, respectively, for computing 3-D gray-level cooccurrence. In addition, a few statistical indexes are also extended to third-order forms in order to calculate quantitative texture properties of the generated cooccurrence tensor field. An airborne hyperspectral data set and an EO-1 Hyperion image are used to test the performance of the developed algorithms. Experimental results indicate that the developed 3-D texture analysis outperforms conventional second-order texture descriptors and the support vector machine-based classifier in supervised classifications of both hyperspectral data sets. [ABSTRACT FROM AUTHOR]