Characterization of spatial texture for use in segmentation of synthetic aperture radar imagery

This study examined the use of textural analysis to assist in the segmentation of synthetic aperture radar (SAR) imagery. A previous study, which dealt with imagery collected passively by electro-optical systems in the visible and infrared portions of the electromagnetic spectrum, tested a method of identifying optimal subsets of texture features from a large pool of possibilities. Three new features were added to this pool and tested against the others to determine their specific utility in the segmentation of SAR imagery. Their value is demonstrated by consistent appearance in optimal subsets of texture features, and by their contribution of up to 8% to image classification accuracy

Evaluation Measures for Hierarchical Classification: a unified view and novel approaches

Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification is the evaluation of different classification algorithms, which is complicated by the hierarchical relations among the classes. Several evaluation measures have been proposed for hierarchical classification using the hierarchy in different ways. This paper studies the problem of evaluation in hierarchical classification by analyzing and abstracting the key components of the existing performance measures. It also proposes two alternative generic views of hierarchical evaluation and introduces two corresponding novel measures. The proposed measures, along with the state-of-the art ones, are empirically tested on three large datasets from the domain of text classification. The empirical results illustrate the undesirable behavior of existing approaches and how the proposed methods overcome most of these methods across a range of cases.Comment: Submitted to journa