Restoration of polarimetric SAR images using simulated annealing

Abstract

Abstract-Filtering synthethic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented applicable to multilook polarimetric SAR images, resulting in an estimate of the mean covariance matrix rather than the RCS. Small windows are applied in the filtering, and due to the iterative nature of the approach, reasonable estimates of the polarimetric quantities characterizing the distributed targets are obtained while at the same time preserving most of the structures in the image. The algorithm is evaluated using multilook polarimetric L-band data from the Danish airborne EMISAR system, and the impact of the algorithm on the unsupervised -classification is demonstrated.

Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation

“…One of the main advantages of the proposed IDAN technique relies on the fact that homogeneity is estimated using the three POLSAR or six POL-InSAR intensities, which are directly available with a very small number of looks. More complex statistical measures, based on the polarimetric or interferometric information [48] could be used, but they would require a much higher number of initial looks. In the POLSAR case, the criterion that merges the coherency matrix diagonal elements (17) allows to take an important part of the polarimetric information into account [49].…”

Polarimetric sar speckle noise model

“…The measured phase can be coded in the complex unitary circle as a unit amplitude phasor (13) In this case, only the real part of (13) will be analyzed, since the imaginary part phase noise model can be derived in the same way. Based on the additive phase noise model (12), the real part of (13) can be decomposed as (14) In (14), the phase term has been separated from the noise contribution . The terms and are only due to the noise term .…”

“…These approaches are characterized for keeping the spatial resolution but loosing all the polarimetric information. The second group contains all those techniques based on obtaining the speckle-free input images, therefore maintaining the correlation information between them [8], [12]. This second group of techniques is basically based on an spatial processing of the SAR images, therefore, affecting the spatial resolution.…”

Subsurface Sensing of Buried Objects Under a Randomly Rough Surface Using Scattered Electromagnetic Field Data

“…Finally, we note that a locally converging decent-type method was employed in this paper. Although the technique we used for initializing the routine worked well for the applications considered here, we recognize that a last area of future work is the development and validation of global type parameter estimation methods (simulated annealing [59]- [61], genetic methods [62]- [64], particle swarm optimization [65], [66]) either to solve the whole inverse problem, or as a tool for initializing the Levenberg-Marquardt approach.…”