Compressed sensing can be a valuable method with which to acquire high-resolution images, reducing the stored amount of information. This objective may be pursued without using any prior knowledge of the images, unlike the standard information compression algorithms do. Information compression can be obtained by a simple matrix multiplication, but the process of reconstructing the original image could be very expensive in terms of computation requirements. We are interested in comparing different reconstruction techniques for compressed air-to-air inverse synthetic aperture radar images, looking for a sensible compromise between performance results and complexity. In more detail, the compared algorithms are iterative thresholding, basis pursuit and convex optimization. Furthermore, particular attention has been devoted to a more appropriate way of splitting large-sized images in order to obtain smaller matrices with uniform sparseness for reducing the computational load.
Many approaches to short-term forecasting the motion of rain structures widely rely on correlation between radar rain maps using local rain intensities. A different approach can be taken in considering rain structures as the base for analysis, while local rain intensities only serve the purpose of detecting, locating and shaping the former. RBF (equalsRadial Basis Function) Neural Networks (NN) provide a means of implementing such approach. Rain maps submitted to RBF NN for training results in turning them into sets of parameters describing observed rain structures. Reiterating the training on time series of maps results in time series of parameters possibly depicting typical trends. Forecasting such parameters and translating forecasted values back into maps should provide a forecast of rain distribution in the near future. We found the best forecasting strategy to be a mix where some of the parameters are forecasted linearly and some else using more RBF NNs. We got further improvement by using GRBF (equalsGradient RBF) in place of RBF in forecasting phase, and making the synthesis phase more stable and reliable by introducing some novelties into the algorithm. In this paper we explain the technique we developed and evaluate the results we obtained.
The automatic analysis of Ground Penetrating Radar (GPR) images is an interesting topic in remote sensing image processing, since it involves the use of pre-processing, detection and classification tools with the aim of near-real time or at least very fast data interpretation. However, actual chains of preprocessing tools for GPR images do not consider usually denoising, essentially because most of the successive data interpretation is based on single radar trace analysis. So, no speckle noise analysis and denoising has been attempted, perhaps assuming that this point is immaterial for the following interpretation or detection tools. Instead, we expect that speckle denoising procedures would help. In this paper we address this problem, providing a detailed and exhaustive comparison of many of the statistical algorithms for speckle reduction provided in literature, i.e. Kuan, Lee, Median, Oddy and wavelet filters. For a more precise comparison, we use the Equivalent Number of Look (ENL), the Variance Ratio (VR). Moreover, we validate the denoising results by applying an interpretation step to the pre-processed data. We show that a wavelet denoising procedure results in a large improvement for both the ENL and VR. Moreover, it also allows the neural detector to individuate more targets and less false positive in the same GPR data set.
In this paper a method for extracting objects from meteorological stereo satellite images and matching parts of them is presented. Great emphasis is put on the choice of the threshold value for object extraction. The matching method is based on a modal matching algorithm: the objects to be matched are sampled and ideally turned into elastic objects. A finite element method allows computing modes of vibration for such elastic body. By comparing displacements due to vibrations in each point from both pictures it is possible to match corresponding parts in the two different viewpoints.
In this paper excellent results on rain pattern tracking are achieved by merging the information extracted from a weather radar data sequence by means of a correlation and a shape analysis approach. The two methods, namely the Tracking Radar Echoes by Correlation corrected by Continuity (COTREC) and the modal matching shape representation are jointly applied to the problem. This choice allows to exploit the advantages of both a global and a more local approach to a motion-based interpretation of rain events. Some results are shown, extracted from data sequences recorded by a C-band weather radar in Northern Italy.
In this work we present the application of the recently introduced modal matching technique to meteorological radar data. Showing how it is possible to retrieve, by means of a suitable data processing, more information about rainfall patterns and their evolution. The advantages for shape analysis and recognition of this algorithm, based on the evaluation of the eigenvalues of the dynamic equilibrium equation, are shown. Moreover, shape analysis allows to integrate data from different sources at different scales. Therefore, accurate global analysis and forecasts of rainfall movements can be achieved. The characteristics and implementation problems of this technique are also addressed, as well as some examples of complex artificial shape data. Finally, a case study of a rain even occurred in Northern Italy is shown.
In this paper a robust processing chain for meteorological data handli11g is pmposed. The chain relies on a shape analysis technique, called modal matching, that is applied to the characterization and tracking of meteorological features from satellite images. Results show that a precise correspondence between point pairs in successit1e frames of a sequence can be achiet<ed, enabling to study the complex movements of meteorological structures in greater detail. Moreover, a technique to determine intermediate (not recorded) frames in a sequence is offered, useful for nowcasting and short-term. forecasting issues.
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