A new spatially adaptive shrinkage approach based on
the nonsubsampled contourlet transform (NSCT) to despeckling
synthetic aperture radar (SAR) images is proposed. This method
starts from the existing stationary wavelet transform (SWT)–domain
Gamma-exponential likelihood model combined with a local spatial
prior model and extends the model further for despeckling an SAR
image via spatially adaptive shrinkage in the NCST domain. The
proposed NSCT-domain shrinkage estimator consists of a new likelihood
ratio function and a new prior ratio function, both of which are
dependent on the estimated masks for the NSCT coefficients.
The former is established by the Gamma distribution with variable
scale and shape parameters and the exponential distribution with
variable scale parameter to adapt the shrinkage estimator to the
redundancy property of the NSCT. Parameters of these two distributions
are estimated by using moment-based estimators. The
latter is equipped with directional neighborhood configurations to
accommodate the estimator to the flexible directionality of the
NSCT, and thus to enhance the detail fidelity. We validate the
proposed method on real SAR images and demonstrate the
excellent despeckling performance through comparisons with the
SWT-based counterpart, two classical spatial filters, and the contourlet
transform-based despeckling technique.
A supervised multiscale image segmentation method is presented based on one class support vector machine (OCSVM)
and wavelet transformation. Wavelet coefficients of training images in the same directions at different scale are organized
into tree-type data as training samples for OCSVMs. Likelihood probabilities for observations of segmentation image can
be obtained from trained OCSVMs. Maximum likelihood classification is used for image raw segmentation. Bayesian rule
is then used for pixel level segmentation by fusing raw segmentation result. In experiments, synthetic mosaic image, aerial
image and SAR image were selected to evaluate the performance of the method, and the segmentation results were
compared with presented hidden Markov tree segmentation method based on EM algorithm. For synthetic mosaic texture
images, miss-classed probability was given as the evaluation to segmentation result. The experiment showed the method
has better segmentation performance and more flexibility in real application compared with wavelet hidden Markov tree
segmentation.
We present a new approach to edge detection on synthetic aperture radar (SAR) images based on contourlet-domain hidden Markov tree (CD-HMT) model. In the contourlet transform, a double filterbank structure, pyramidal directional filterbank, is employed by first using Laplacian pyramidal decomposition and then a local directional filterbank. Compared with the wavelet transform, the contourlet transform not only can capture multiresolution and local information of an image, but obtain its directional information in a flexible way by using different number of directions at different scales. This non-separable two-dimensional transform is a new alternative to and improvement on separable wavelets for the representation of an image. On the other hand, HMT is a tree-structured probabilistic graph that can capture the statistical properties of contourlet coefficients at different scales and directions where each coefficient is considered as an observation of its hidden state variable which indicates whether the coefficient belongs to singularity structures or not. Herein, the state "1" represents the location belonging to singularity structure, and state "0" not. CD-HMT model is firstly trained by Expectation-Maximization (EM) algorithm before the Viterbi algorithm is utilized to uncover the hidden state sequences based on maximum a posterior (MAP) estimation. Moreover, we take into account the effect of speckle on the detection performance for singularity structures. Finally, the thinning post-processing procedure is performed to obtain the edge map of an SAR image. Experiments on both simulated speckled and real SAR images demonstrate the feasibility and effectiveness of our approach with the performance outperforming the classical Canny edge detector.
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