1,023 research outputs found
Someone You Know: A Firend’s Farewell, By Maria Pallotta-Chiarolli
No full textThis review of Maria Pallotta-Chiarolli’s book Someone You Know is undertaken from an analytical perspective, from my personal response as the sister-in-law of a young man who died from AIDS, and as a former PhD candidate of the author. This book is a biography that chronicles the journey, from the mid to late 1980s, of Maria alongside her friend Jon, as Jon finds out he is HIV-positive and subsequently becomes unwell, eventually dying from AIDS-related complications
Fine mapping of the LMA QTL on chromosome 7B
No full textK. Mrva, M. A. Pallotta, K. Oldach, T. Schnurbusch, H. Y. Law and D. J. Mare
Detecting covariance symmetries for classification of polarimetric SAR images
No full textThe availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image
Coregistration Method for Rotated/Shifted FOPEN SAR Images
No full textThis paper tests a SAR image coregistration method, developed to account for a joint rotation and range/azimuth shift effect in absence of zooming, on foliage penetrating (FOPEN) data. In particular, the method is referred as a constrained Least Squares (CLS) optimization method and, in its basic form, it sharply extracts all patches composing the entire image. Differently, in next developments it applies a detection stage to identify extended areas in the images where patches are then selected. Moreover, it also performs a refinement of the equations in the CLS problem through an iterative cancellation procedure. The performance of this enhanced version of the CLS are made on the challenging Carabas-II VHF-band FOPEN SAR data to demonstrate its effectiveness also in high-resolution SAR images
SAR Coregistration by Robust Selection of Extended Targets and Iterative Outlier Cancellation
No full textThis letter extends the constrained Least Squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS
Pseudo-Zernike moments based radar micro-Doppler classification
No full textReliable micro-Doppler signature classification requires the use of robust features describing uniquely the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper the application of the pseudo-Zernike moments for micro-Doppler classification is introduced demonstrating the effectiveness of the proposed approach by classifying real data. The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients
A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal pseudo-Zernike polynomials
No full textPhase modulation induced by target micro-motions introduces side-bands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that is capable of uniquely describing the micro-motion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists in the extraction of the pseudo-Zernike moments from the Cadence Velocity Diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes
Classification of covariance symmetries in full-polarimetric SAR images
No full textThis chapter has dealt with the problem of covariance matrix classification in PolSAR images on the base of the special structures assumed under symmetrical properties of the returns associated to the pixels under test. In particular, the chapter has focused on both homogeneous and heterogeneous SAR images' classification, including a description of the symmetry classification within the PolInSAR imagery. For all the described frameworks, the problem has been formulated as a multiple hypothesis test comprising both nested and non-nested hypotheses. For this reason, it has been solved by resorting to the well-known MOS rules to overcome the limitations of the classic GML approach.
Results conducted on both simulated and L-band real-recorded PolSAR data have proven the effectiveness of the described methodologies, thus paving the way for further applications, e.g., as a preliminary step of a more sophisticated two-stage algorithm aimed at, for instance, classifying the scene
Loading Factor Estimation under Affine Constraints on the Covariance Eigenvalues with Application to Radar Target Detection
No full textMaximum likelihood (ML) estimation of the loading factor under affine constraints on the covariance eigenvalues is addressed. Several situations of practical interest for radar are considered, and the corresponding ML solutions to the loading factor estimation problem are derived in closed form. Furthermore, it is shown that the constrained ML problem, the constrained geometric approach, and the constrained problem of mean square error minimization (with respect to the loading factor) all lead to the same solution. At the analysis stage, the effectiveness of the resulting covariance estimators is evaluated in terms of both the signal-to-interference-plus-noise ratio and the receiving beampattern shape and compared with that of other covariance estimation methods available in the literature. Finally, a receiving architecture based on the adaptive matched filter that exploits the new loaded covariance estimators is also considered to assess the benefits of the new strategies in terms of detection probability
- …
