An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

Variability of Soft X—ray Spectral Shape in Blazars Observed by ROSAT

1 INTRODUCTION Blazars, including BL Lac objects, highly polarized and optically violently variable quasars,and flat-spectrum radio quasars (FSRQs), are characterized by highly variable non-thermalemission which dominates their characteristics from radio to y-ray bands. The mechanismbelieved to be responsible for their broadband emission is synchrotron radiation followed by in-verse Compton (IC) scattering at higher energies (e.g. Blandford & Konigl 1979). Relativisticbeaming of a jet…