Boli Xiong

A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images

With its weather- and illumination-independent characteristics, synthetic aperture radar (SAR) has become an important tool for change detection. There are two critical steps in SAR image change detection: designing a change detector and choosing a decision rule. Given a measure from a change detector, the change detection results could be sensitive to the decision rule, such as the selection of a threshold. This letter presents a change detection measure based on a likelihood ratio and the statistical distribution of SAR intensity images. The likelihood ratio is defined as the ratio between the joint probability density functions (PDFs) of a pair of SAR images. Under the condition that both PDFs follow the gamma distribution, the histogram of this change detection measure deduced from the likelihood ratio has a single and steep peak that can be used to reliably and easily determine the change detection threshold. Analyses of SAR image pairs from different platforms show that the proposed change detection measure is simple and effective in detecting changes.

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.

Estimation of the Repeat-Pass ALOS PALSAR Interferometric Baseline Through Direct Least-Square Ellipse Fitting

The precise estimation of the baseline is a crucial procedure in repeat-pass interferometric synthetic aperture radar (InSAR) applications. Using the ephemeris of the satellite, a polynomial regression algorithm can fit the satellite orbit at the third or higher order with a main shortcoming that the mutual constraints among the three dimensions defining the orbit are missed. In this paper, a new approach is presented to fit the satellite orbit based on the assumption that the satellite orbit is a 3-D ellipse, which retains the relations among the three dimensions. Considering the complexity of 3-D ellipse parameters estimation, the 3-D orbit is first transformed into three 2-D ellipses. Then, the parameters of these 2-D ellipses are estimated with a direct least-square ellipse fitting method (DLS-EFM). These two orbit fitting algorithms are tested with ten sets of advanced land observation satellite phased array L-band SAR data, which were acquired in north Toronto, Ontario, Canada, from September, 2008 to January, 2009. Moreover, two of them acquired with an adjacent period were chosen to form a repeat-pass InSAR, and the corresponding baseline is calculated with the proposed method as an example. The experimental results show that the error of the satellite position using DLS-EFM is at a submetric level, which is less than one-tenth of that of the polynomial regression algorithm. Consequently, the proposed method is appropriate for the baseline estimation in spaceborne InSAR applications.

SAR Azimuth ambiguities removal for ship detection using time-frequency techniques

In this paper, a new azimuth ambiguities removal method is introduced for ship detection by Time-Frequency (TF) analysis. A TF coherence indicator is proposed to filter ghost echoes due to the different TF coherence characteristics between real ship target echoes and ambiguous ones. The effectiveness of this proposed TF coherence indicator for ship detection is demonstrated using single polarimetric spaceborne TerraSAR-X coherent data over the test sea/ocean site in Hongkong, China.

Registration for SAR and optical images based on straight line features and mutual information

This paper proposes a novel registration method for optical and SAR images which is based on straight line features and mutual information. Firstly, different edge detectors are employed to detect the line segments in both optical and SAR images respectively. Then, through the Hough transform and a straight line fitting and filtration method, the main straight lines of each image are extracted and their intersections are obtained and taken as the candidate matching points. With the RANSAC (RANdom SAmpling Consensus) method, corresponding point pairs (CPPs) are found with these candidate points and a coarse registration between the heterogeneous images is implemented. At last, by using the mutual information of the separated patches generated from the coarse registered images, a fine registration result is finally achieved. The experiment with a pair of X-band air-borne SAR and optical images validates the efficiency and precision of the proposed method.

A geometric parameter extraction method of ship target based on an improved snake model

It is the basis of realization ship recognition to accurately extract geometric parameters of the ship target in synthetic aperture radar (SAR) images. Due to the unique SAR imaging mechanism, speckle noise, azimuth ambiguity and side lobe effect seriously impact on geometric parameter estimation of the ship target. Therefore, a method is presented to extract geometric parameters of the ship based on ellipse fitting and the gradient vector flow (GVF) snake with shape priors.

Affine invariant shape projection distribution for shape matching using relaxation labelling

Shape is considered to be one of the most promising tools to represent and recognise an object. In this study, an effective and rigorous shape matching algorithm is developed based on a new descriptor and relaxation labelling technique. For each contour point, the descriptor captures the distribution of all points within the shape region along the vector perpendicular to that from the centroid to the point. In addition to stable affine invariance, the descriptor is robust to noise since it makes use of all points in the shape region. The descriptor distance is used to initialise the contour point matching probability, and relaxation labelling technique is utilised to update the matching probability using a new compatibility coefficient function, which is defined based on the shape projection preserving characteristic. The experiments on synthetic and real remote sensing data are provided to test the performance of the authors’ proposed algorithm. Compared to other four state-of-the-art contour-based shape matching algorithms, their algorithm is more robust and capable of shape matching under affine transformations and noise.

Evaluation of different SAR change detectors based on vehicle recognition with MSTAR data set

This letter presents an approach to evaluate the performance of synthetic aperture radar (SAR) change detectors based on target recognition with the moving and stationary target acquisition and recognition (MSTAR) data set. The goal of this study is to develop an assessment method to objectively evaluate the performance of different change detectors. This approach utilizes the minimal differences between the residual data generated by four change detectors to recognize the vehicle types. By comparisons of the recognition rate and time needed to complete the recognition of MSTAR data set, the performance of each change detector can be objectively assessed and a convictive conclusion can be drawn on which change detector is better. The four involved change detectors are the difference change detector (DiCD), ratio change detector, log-ratio change detector and likelihood ratio change detector (LiRCD), respectively. The experimental results show that the LiRCD is with the highest recognition rate, whereas the DiCD is with the lowest recognition rate, which indicates the LiRCD is more appropriate than the other three SAR change detectors in the changed information preservation and speckle noise suppression.

A Hierarchical Extension of General Four-Component Scattering Power Decomposition

The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering power decomposition method (G4U) is presented. The conventional G4U is first proposed by Singh et al. and it has advantages in full use of information and volume scattering characterization. However, the OVS still exists in the G4U and it causes a scattering mechanism ambiguity in some oriented urban areas. In the proposed method, matrix rotations by the orientation angle and the helix angle are applied. Afterwards, the transformed coherency matrix is applied to the four-component decomposition scheme with two refined models. Moreover, the branch condition applied in the G4U is substituted by the ratio of correlation coefficient (RCC), which is used as a criterion for hierarchically implementing the decomposition. The performance of this approach is demonstrated and evaluated with the Airborne Synthetic Aperture Radar (AIRSAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Radarsat-2, and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) fully polarimetric data over different test sites. Comparison studies are carried out and demonstrated that the proposed method exhibits promising improvements in the OVS and scattering mechanism characterization.