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The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset). [ABSTRACT FROM AUTHOR]

This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly on the complex-valued image data, but also on spatially varying probability distributions and thus allow a seamless integration of RFs within the stacking framework. Experimental results show that the classification performance is consistently improved by the proposed approach, i.e., the achieved accuracy is increased by 4% and 7% for one fully- and one dual-polarimetric dataset. This increase only comes at the cost of a linear increased training and prediction time, which is rather limited as the method converges quickly. [ABSTRACT FROM AUTHOR]

The fundamental analysis of drop coalescence probability in liquid/liquid systems is necessary to reliably predict drop size distributions in technical applications. For this crucial investigation two colliding oil drops in continuous water phase were recorded with different high speed camera set-ups under varying conditions. In order to analyze the huge amount of recorded image sequences with varying resolutions and qualities, a robust automated image analysis was developed. This analysis is able to determine the trajectories of two colliding drops as well as the important events of drop detachment from cannulas and their collision. With this information the drop velocity in each sequence is calculated and mean values of multiple drop collisions are determined for serial examinations of single drop collisions. Using the developed automated image analysis for drop trajectory and velocity calculation, approximately 1–2 recorded high speed image sequences can be evaluated per minute. [ABSTRACT FROM AUTHOR]

In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the traditional Wishart classifier and the more sophisticated Random Forest. Extensive qualitative and quantitative results are shown on a fully polarimetric and multi-baseline dataset from the E-SAR sensor from the German Aerospace Center (DLR). [ABSTRACT FROM AUTHOR]

Abstract: Image analysis has become a powerful tool for the work with particulate systems, occurring in chemical engineering. A major challenge is still the excessive manual work load which comes with such applications. Additionally manual quantification also generates bias by different observers, as shown in this study. Therefore a full automation of those systems is desirable. A MATLAB® based image recognition algorithm has been implemented to automatically count and measure particles in multiphase systems. A given image series is pre-filtered to minimize misleading information. The subsequent particle recognition consists of three steps: pattern recognition by correlating the pre-filtered images with search patterns, pre-selection of plausible drops and the classification of these plausible drops by examining corresponding edges individually. The software employs a normalized cross correlation procedure algorithm. The program has reached hit rates of 95% with an error quotient under 1% and a detection rate of 250 particles per minute depending on the system. [Copyright &y& Elsevier]