Yongmei Jiang

A Threshold Selection Method Using Two SAR Change Detection Measures Based on the Markov Random Field Model

This letter presents a threshold selection method in change detection (CD) with synthetic aperture radar (SAR) images, which combines the characteristics of two different CD measures by using the Markov random field model. One is the well-known log-ratio CD measure, and the other is derived from the likelihood ratio and is based on the statistical properties of SAR intensity images. The proposed unsupervised CD algorithm overcomes the shortcomings and strengthens the advantages of these two measures. The experimental results with two pairs of SAR images show that the proposed algorithm is effective and better than the algorithms using the two aforementioned CD measures.

Polarimetric Scattering Similarity Between a Random Scatterer and a Canonical Scatterer

In this letter, we propose a novel parameter to measure the scattering similarity between a random scatterer and a canonical scatterer. Compared with the similarity parameter proposed by Yang, the novel parameter not only has some advantages, such as its independence of the spans of a coherence matrix, but also can be applied directly in the case of a random scatterer made up of multiscattering centers. As an example, the novel parameter is adopted to extract some scattering characteristics of a target. With the full polarimetric L-band airborne synthetic aperture radar data, we illustrate the veracity of the novel parameter in measuring scattering similarity and its application in terrain classification.

Automated flood detection with improved robustness and efficiency using multi-temporal SAR data

Flood detection from synthetic aperture radar (SAR) images should be performed with accurate, stable, automated and time-efficient algorithms; however, few methods meet all these requirements. Recently, Giustarini et al. proposed an automated promising methodology, capable of providing satisfactory results in flood detection. The algorithm is based on the assumption that a flood image contains a relatively high number of pixels with low backscatter values, exhibiting a bimodal histogram. For the case of a histogram that is not bimodal, the optimization of the theoretical curve describing the water pixels has to be manually constrained in a user-defined range. To overcome this shortcoming, this letter proposes an alternative procedure for core water body identification. First, by thresholding the difference image, derived by change detection between the flood and reference images, a mask of core water bodies is identified. Then, the mask is applied on the flood image, to extract the water pixels located in the core water bodies and straightforwardly derive the statistical curve describing water pixels. Successively, a sequence of thresholding, region growing and change detection is applied. The experimental results with two pairs of SAR images show that the proposed automated algorithm is stable and time-efficient, and provides accurate results.

Contour matching using the affine-invariant support point set

Moment has been widely used for contour matching. To use the moment to achieve contour matching under affine transformations, the affine-invariant support point set (SPS) should be constructed first. Then, a novel method of acquiring SPS based on the contour projection (SPS-CP) is proposed here. For an arbitrary selected contour point, the contour is projected onto the line vertical to the vector connecting the contour centroid and the selected point, and the contour points with the sampled projection values are picked up to form the SPS-CP of the point. SPS-CP which captures the global structure of the contour is stably affine-invariant. Experiments on synthetic and real data demonstrate that moments generated from SPS-CP outperform those generated from SPSs sampled by uniform spacing or affine length.

SAR image segmentation combining the PM diffusion model and MRF model

This paper addresses the statistical segmentation of SAR (Synthetic Aperture Radar) image combining PM (Perona Malik) nonlinear diffusion model and MRF (Markov Random Field) model. First, the original SAR image is filtered using the modified PM nonlinear diffusion model, in which the diffusion coefficients along the tangent direction and the normal direction are approximated and simplified. Afterwards, the filtered image is segmented using MRF model, in which the clique potential is computed using both the label configuration and the intensity information. The proposed method is marked by PM-MRF for short. Experimental results show that PM-MRF competes favorably with the traditional one to segment SAR image homogeneously.

An Optimization Procedure of the Lagrange Multiplier Method for Polarimetric Power Optimization

The Lagrange multiplier method is one of the basic optimization procedures to find the optimum polarizations for the incoherent scattering case. This letter proves for the first time that a fixed relationship exists between the optimum polarization and the Lagrange multiplier. Then, an optimization procedure is proposed to simplify the computational complexity of the Lagrange multiplier method. To speed up the convergence of the proposed procedure, the minimum search intervals are discussed and given theoretically. A numerical example is shown to demonstrate the effectiveness of the proposed procedure.

A method of acquiring tie points based on closed regions in SAR images

This paper presents a method in finding tie points automatically in synthetic aperture radar (SAR) image pairs based on the extracted closed regions. There are mainly three steps during this process. The first step is to extract the closed regions in the SAR image with an image segmentation approach deducted by the geodesic active contour (GAC) model. Then, a polygonal approximation process is adopted to locate the feature points on the boundaries of these regions. With the obtained feature points, geometric hashing theory is employed to match these feature points as the tie points. A pair of simulated SAR images and a pair of high-resolution airborne SAR images are used to test and evaluate the proposed method. The experimental results show that the proposed method is effective and appropriate for the acquisitions of tie points in SAR image pairs.

An affine invariant relative attitude relationship descriptor for shape matching based on ratio histograms

A novel shape descriptor, named as ratio histograms (R-histogram), is proposed to represent the relative attitude relationship between two independent shapes. For a pair of two shapes, the shapes are treated as the longitudinal segments parallel to the line connecting centroids of the two shapes, and the R-histogram is composed of the length ratios of collinear longitudinal segments. R-histogram is theoretically affine invariant due to collinear distance invariance of the affine transformation. In addition, as the computation of the length ratio weakens the noise contribution, R-histogram is robust to noise. Based on the R-histogram, the shape-matching algorithm includes two major phases: preprocessing and matching. The first phase, which can be processed off-line, is trying to obtain the R-histograms of all original shape pairs. In the second phase, for each transformed shape pair, its R-histogram is computed and the candidate matched shape pair with minimal R-histogram matching error is found. Subsequently, a voting strategy, which further improves the accuracy of shape matching, is adopted for the candidate corresponding shape pairs. Experimental results demonstrate that the proposed R-histogram is robust and efficient.

Harbor detection of remote sensing images based on model

Harbor detection is an important aspect for remote sensing ocean application research. Fast and accurate harbor detection can greatly improve the ability of automatic interpretation for remote sensing image shipside buildings and ships inside ports. By having a detailed research on harbor object disposal, this paper established harbor model and advance knowledge, and proposed a harbor detection method of remote sensing images based on model. Besides, aiming at the shortage that proposed method has a little far in locating harbor range, this paper defined the principal axis of harbor object and calculated harbor circum-rectangle which has a higher location precision. Compared with an existing harbor detection algorithm, our experiments show that novel method has a whole harbor detection, exact location and better universal.

Parameter estimation for SAR moving target detection using Fractional Fourier Transform

This paper proposes an algorithm for multi-channel SAR ground moving target detection and estimation using the Fractional Fourier Transform(FrFT). To detect the moving target with low speed, the clutter is first suppressed by Displace Phase Center Antenna(DPCA), then the signal-to-clutter can be enhanced. Have suppressed the clutter, the echo of moving target remains and can be regarded as a chirp signal whose parameters can be estimated by FrFT. FrFT, one of the most widely used tools to time-frequency analysis, is utilized to estimate the Doppler parameters, from which the moving parameters, including the velocity and the acceleration can be obtained. The effectiveness of the proposed method is validated by the simulation.