Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. In this paper, a new generalized divergence measure, divergence, is proposed. Some properties such as convexity and its upper bound are derived. Based on the Jensen-Renyi divergence, we propose a new approach to the problem of ISAR (Inverse Synthetic Aperture Radar) image registration. The goal is to estimate the target motion during the imaging time. Our approach applies Jensen-Renyi divergence to measure the statistical dependence between consecutive ISAR image frames, which would be maximal if the images are geometrically aligned. Simulation results demonstrate that the proposed method is efficient and effective.
A high-resolution spectral analysis algorithm with application to ISAR (inverse synthetic aperture radar) imaging is proposed in this paper. The ISAR imaging is induced by target motion, which in turn causes time varying spectrum of reflected signals from the target. During the imaging time, the scatterers must remain in their range cells. Optimal Integration Angle need to be estimated to prevent defocusing in cross-range. In order to measure the evolution of spectra, we propose a new information divergence measure based on Renyi entropy. A detailed discussion reveals many of the desirable properties of this new Jensen-Renyi divergence measure. When applied in inspecting time-frequency representation of reflected signals, optimal integration angle can be obtained to produce a well focused and high resolution ISAR image.
In this paper, we propose a new algorithm for extracting a non smooth shape from its noisy observation. The key ideal is to project the noisy shape onto a set of orthogonal subspaces at different resolutions, and construct scale space representation gleaned from the locally smoothed shape. Using the curvature we proceed to filter the high resolution scale subspace by projecting it onto the scale which is in turn used for the reconstruction. Inspired by the conjugate mirror filter and the wavelet decomposition synergy, we propose a curvature based filter operating at different scales and with minimal knowledge about the noise statistics.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.