Discriminative Features and Classification Methods for Accurate Classification

Automated classification and tracking approaches suffer from the high-dimensionality of the data and information space, which frequently rely upon both discriminative feature selection and efficient, accurate supervised classification strategies. Feature selection strategies have the benefit of representing the data in a modified reduced space to improve the efficacy of data mining, machine learning, and computer vision approaches. We have developed feature-selection methods involving feature ranking and assimilation to discover reduced feature sets that produce accurate results in classification for automated classifiers with significant specificity and sensitivity. We have tested a wide range of spatial, texture, and wavelet-based feature sets for electro-optical (EO) aerial imagery and infrared (IR) land-based image sequences on several machine-learning algorithms for classification for performance evaluation and comparison. A detailed experimental evaluation is provided for the classification efficacy of the features and classifiers on the particular data sets, and is accompanied by a discussion of the particular success or failure. In the second section, we detail our novel feature set that combines moment and edge descriptors and produces high, robust accuracy when evaluated for classification. Our method leverages information previously calculated in the detection stage, which includes wavelet decomposition and texture statistics. We demonstrate the results of our feature set implementation and discuss methods for creating classifier decision rules to choose a particular classification algorithm dependent on certain operating conditions or data types adaptively.