In this paper, a novel classification method path-based similarity with instance-level constrains for SemiBoost, PBS-SB
in short is proposed, and we exploit it for synthetic aperture radar automatic target recognition (SAR-ATR). Different
from traditional SemiBoost method that uses the Gaussian kernel similarity, PBS-SB utilizes the path-based similarity,
which considers the global consistence of data clusters. Besides, the instance-level constraints are integrated into the
similarity measurement to construct the semi-supervised similarity, which provides the local consistence information.
The experiments on 5 different data sets and MSTAR (Moving and Stationary Target Acquisition and Recognition)
database demonstrate that the proposed method has superior classification performance with respect to competitive
methods.
In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP)
classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target
recognition by using GP classification with automatic relevance determination (ARD) function. Compared with
k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic
model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that
our algorithm is self-tuning and has better recognition accuracy as well.
This paper presents a new method based on Semantic Structure Tree (SST) for remote sensing image segmentation, in
which, the semantic image analysis is used to construct the SST of the image. The leaves of the SST represent the
semantics of the image and serve as human semantic understanding of the image. The root of the tree is the whole image.
The SST uses grammar rules to construct a hierarchy structure of the image and gives a complete high-level semantics
contents description of the image. Experimental results show that the tree can give efficient description of the semantic
content of the remote sensing image, and can be well used in remote sensing image segmentation.
Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new
algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is
proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster
number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised
spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the
proposed method outperforms other spectral clustering algorithms.
In this paper, we present a novel approach for detecting the changed regions caused by flooding events in multi-temporal
SAR images. And the proposed method concludes two parts: 1) constructing difference image (DI) by fusion strategy
proposed in this paper; 2) producing change-detection map based on FCM and fuzzy degree of nearness. Experimental
comparisons on real multi-temporal SAR images indicate that the proposed method can reduce the affection by speckle
noise. Meanwhile, the proposed method can accurately detect the interested changed regions.
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.
Local Polynomial Approximation-Intersection of Confidence Intervals (LPA-ICI) is a new approach, which can find the
boundary of the isotropic region efficiently, especially for noisy images. This paper presents a novel image denoising
method, adaptive four windows wavelet image denoising based on LPA-ICI, which is composed of three parts: searching
for four adaptive windows with LPA-ICI, updating the noisy wavelet coefficients by hard threshold and obtaining a final
"clean" pixel value by fusing the updated pixels with different weights which are determined by the sparsity of regions.
Experiments show that our algorithm has advanced performance, reconstructed edges are clean, and especially without
unpleasant ringing artifacts.
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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