3,146 research outputs found
An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data
Variable and higher pulse repetition frequencies (PRFs) are increasingly
being used to meet the stricter requirements and complexities of current
airborne and spaceborne synthetic aperture radar (SAR) systems associated with
higher resolution and wider area products. POLYPHASE, the proposed resampling
scheme, downsamples and unifies variable PRFs within a single look complex
(SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to
an effective lower PRF. A sparsity condition of the received SAR data ensures
that the uniformly resampled data approximates the spectral properties of a
decimated densely sampled version of the received SAR data. While experiments
conducted with both synthetically generated and real airborne SAR data show
that POLYPHASE retains comparable performance to the state-of-the-art BLUI
scheme in image quality, a polyphase filter-based implementation of POLYPHASE
offers significant computational savings for arbitrary (not necessarily
periodic) input PRF variations, thus allowing fully on-board, in-place, and
real-time implementation
The Effects of Signal and Image Compression of SAR Data on Change Detection Algorithms
With massive amounts of SAR imagery and data being collected, the need for effective compression techniques is growing. One of the most popular applications for remote sensing is change detection, which compares two geo-registered images for changes in the scene. While lossless compression is needed for signal compression, the same is not often required for image compression. In almost every case the compression ratios are much higher in lossy compression making them more appealing when bandwidth and storage becomes an issue. This research analyzes different types of compression techniques that are adapted for SAR imagery, and tests these techniques with three different change detection algorithms. Many algorithms exist that allow large compression ratios, however, the usefulness of the data is always the final concern. It is necessary to identify compression methods that will not degrade the performance of change detection analysis
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model
This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images
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