PULSAR: Parallel noise despeckling of SAR images

Addison, Cliff; Appiani, E.; Cook, R. D.; Corvi, M.; Harms, J.; Howard, G.; Stephens, Benjamin R.

doi:10.1007/3-540-61142-8_545

Cited by 1 publication

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We therefore define the neighboring CM's by means of the original central sample CM, i.e., , with the factors being real scalars. Solving the log likelihood equation with respect to these factors, i.e., , yields (27) This shows, that the overall highest probability (26) is obtained, if the neighbors within the connection filter are given as . The mean CM estimate from this specific filter is biased as , and to derive the expression of the bias resulting from this filter, we insert in the MLE for the mean CM (17) giving (28) This expression only depends on the number of looks of the original data, , on the number of neighbors within the connection filter, , and on the dimension of the CM, .…”

Section: Bias Of the Mean CM Estimatementioning

confidence: 97%

“…The number of iterations used for a specific application is obviously a compromise between the desired degree of speckle reduction and the work load of the restoration. It should be noted, that an almost linear relation between the speed-ups and the number of processors is expected if applying parallel computing to the restoration algorithm [27].…”

Section: Speckle Reductionmentioning

confidence: 99%

See 1 more Smart Citation

Restoration of polarimetric SAR images using simulated annealing

Schou

Skriver²

2001

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Bias Of the Mean CM Estimatementioning

confidence: 97%

Section: Speckle Reductionmentioning

confidence: 99%