The typical probability based point pattern matching method is coherent point drift (CPD) algorithm, which treats one point set as centroids of a Gaussian mixture model, and then fits it to the other. It uses the expectation maximization framework, where the point correspondences and transformation parameters are updated alternately. However, the anti-outlier performance of CPD is not robust enough as outliers have always been involved in the operation until the CPD converges. Hence, an automatic outlier suppression (AOS) mechanism is proposed. First, outliers are judged by a matching probability matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the Gaussian centroids are forced to move coherently by this transformation model. AOS-CPD can efficiently improve the anti-outlier performance of rigid CPD. Furthermore, CPD is applied to image matching. A new local changing information descriptor-relative phase histogram (RPH) is designed and RPH-AOS-CPD is proposed to embed RPH measurement into AOS-CPD as a constraint condition. RPH-AOS-CPD makes full use of grayscale information besides having an excellent anti-outlier performance. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust to outliers and RPH-AOS-CPD offers a good practicability and accuracy in image matching applications.
Point pattern matching (PPM) including the hard assignment and soft assignment approaches has attracted much attention.
The typical probability based method is Coherent Point Drift (CPD) algorithm, which treats one point set(named model
point set) as centroids of Gaussian mixture model, and then fits it to the other(named target point set). It uses the
expectation maximization (EM) framework, where the point correspondences and transformation parameters are updated
alternately. But the anti-outlier performance of CPD is not robust enough as outliers have always been involved in
operation until CPD converges. So we proposed an automatic outlier suppression mechanism (AOS) to overcome the
shortages of CPD. Firstly, inliers or outliers are judged by converting matching probability matrix into doubly stochastic
matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the model point set is forced
to move coherently to target point set by this transformation model. The transformed model point set is imported into EM
iteration again and the cycle repeats itself. The iteration finishes when matching probability matrix converges or the
cardinality of accurate matching point set reaches maximum. Besides, the covariance should be updated by the newest
position error before re-entering EM algorithm. The experimental results based on both synthetic and real data indicate that
compared with other algorithms, AOS-CPD is more robust and efficient. It offers a good practicability and accuracy in
rigid PPM applications.
This paper investigates the problem of detecting changes on multitemporal SAR imagery in an unsupervised way. A
novel change indicator was developed to identify the temporal changes. It is computed by the local average of the
amplitude ratio comparing the exponentiation of the local average of the logarithm–transformed amplitude ratio.
Compared with the classical ratio of local means, the novel operator is more effective in identifying the changed pixels
even the local means are preserved. The classification is implemented by an automatic thresholding algorithm derived
from a new Riemannian metric defined in the differential geometry structure. The geodesic distance derived from the
new Riemannian metric provides a way to compare the distance between the probability distributions of the changed
class and the non-changed class. The probability density functions of the changed and non-changed classes are
characterized over the photometric variable. By maximizing the distance between the probability density distributions of
the two classes, the misclassification errors are minimized and the optimal threshold is achieved accordingly.
Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The obtained accuracies confirm the
effectiveness of the proposed approach.
In this paper, we propose an unsupervised change detection method using the labeled co-occurrence matrix on multitemporal
SAR images. In SAR images, each land cover (LC) class has a distinct reflectivity to radar signals and presents
a specific backscattering value. Generally, the amplitude of the SAR images can be roughly clustered into three classes
according to the backscattering behaviors of the LC classes. The changes occurred between the images can be considered
as a backscattering variation that is changed from one backscattering class into another. As a result, we analyzed the
possible cases of the positive and negative backscattering variations, and merged the initial three backscattering classes
into two classes with the pixel in the medium backscattering class being attached to the strong backscattering class and
the low backscattering class respectively in a membership degree. Two pairs of fuzzy-label images are derived
accordingly, where each pair of fuzzy-label images are computed from the multi-temporal SAR data. The labeled cooccurrence
matrix is computed locally on each pair of fuzzy-label images by combining the membership values in a
conjunctive operator, and the autocorrelation feature is extracted. The classifications are implemented by Otsu Nthresholding
algorithm on the derived two autocorrelation features. The final binary change detection map is achieved by
combining the obtained two classification results. Experiments were carried on portions of multi-temporal Radarsat-1
SAR data. The effectiveness of the proposed approach was confirmed.
This paper investigates an imaging method for space debris by wideband radar. Because of the spinning of the space
debris, the correlation of the adjacent high range resolution profile (HRRP) is undermined and the motion compensation
method for dechirped echoes is invalid. Therefore, a wideband imaging method of space debris based on intermediate
frequency sampling (DIFS) signals is proposed in this paper. The IF sampling technique has the advantage in
maintaining the coherence of echo pulse, which eliminates the negative influence of the spin. Firstly, the accurate
translational motion parameters of the target are estimated from the radar observations by using of polynomial fitting
method. Then the translational motion compensation is carried out in frequency domain based on the target motion track.
Finally, the improved back projection transform (BPT) method is used for image reconstruction, which transforms the
echo from range-time domain to the scattering point distribution plane by coherent integral. A well-focused and high
resolution image of the space debris without side lobe peaks can be obtained in the end. The simulation results indicate
the validity of the proposed method in this paper.
Migration through resolution cells (MTRC) is generated in high-resolution inverse synthetic aperture radar (ISAR)
imaging. A MTRC compensation algorithm for high-resolution ISAR imaging based on improved polar format algorithm
(PFA) is proposed in this paper. Firstly, in the situation that a rigid-body target stably flies, the initial value of the
rotation angle and center of the target is obtained from the rotation of radar line of sight (RLOS) and high range
resolution profile (HRRP). Then, the PFA is iteratively applied to the echo data to search the optimization solution based
on minimum entropy criterion. The procedure starts with the estimated initial rotation angle and center, and terminated
when the entropy of the compensated ISAR image is minimized. To reduce the computational load, the 2-D iterative
search is divided into two 1-D search. One is carried along the rotation angle and the other one is carried along rotation
center. Each of the 1-D searches is realized by using of the golden section search method. The accurate rotation angle
and center can be obtained when the iterative search terminates. Finally, apply the PFA to compensate the MTRC by the
use of the obtained optimized rotation angle and center. After MTRC compensation, the ISAR image can be best focused.
Simulated and real data demonstrate the effectiveness and robustness of the proposed algorithm.
This paper proposes a new image registration method based on grade-by-grade matching in interferometric inverse
synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the
offset between two ISAR images for different antennas along each baseline is analyzed. Strong scatterer centers (SSCs)
are extracted from the ISAR images of each antenna by OTSU method firstly. A standard matching is calculated by the
image centroid. Then a mapping of region of interest (ROI) and correlation is carried out to get the precise registration.
Simulation results demonstrate that the offset between two ISAR images can be compensated effectively when the
proposed method is used, achieving a high quality 3D InISAR image consequently.
The characterization of urban environments in synthetic aperture radar (SAR) images is becoming increasingly
challenging with the increased spatial ground resolutions. In SAR images having a geometrical resolution of few meters
(e.g. 3 m), urban scenes are roughly speaking characterized by three main types of backscattering: low intensity, medium
intensity, and high intensity, which correspond to different land-cover types. Based on the observations of the behavior
of the backscattering, in this paper we propose the labeled co-occurrence matrix (LCM) technique to detect and extract
built-up areas. Two textural features, autocorrelation and entropy, are derived from LCM. The image classification is
based on a similarity classifier defined in the general Lukasiewicz structure. Experiments have been carried out on
TerraSAR-X images acquired on Nanjing (China) and Barcelona (Spain), respectively. The obtained classification
accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas compared
with the traditional grey level co-occurrence matrix (GLCM) texture features.
Image registration is a fundamental and crucial step in remote sensing image analysis. However, it is known that image
registration method is application-based. The type and content of remote sensing images affect the choice of image
registration methods. Previous image registration task took experts to manually choose the image registration elements.
This paper presents a self-adaptive image registration method which could automatically choose registration elements
which are more appropriate for remote sensing images under processing. The proposed method first chooses several
local regions for the representation of the whole image, and then different registration elements are tested on these local
regions. The local registration results are evaluated and the registration of the whole image is done with learned
registration elements from local registrations. The registration chain is automatic; therefore it is a self-adaptive
registration method. The proposed method is demonstrated on several real remote sensing image pairs, and its feasibility
and superiority are proved by the results.