Gongjian Wen

A robust similarity measure for attributed scattering center sets with application to SAR ATR

This paper proposes a robust similarity measure for two attributed scattering center (ASC) sets and applies it to synthetic aperture radar (SAR) automatic target recognition (ATR). The extraction uncertainty of an individual ASC is modeled by an adaptive Gaussian distribution according to its attributes. Then the distance between two individual ASCs is defined as the Kullback-Leibler (KL) divergence between two Gaussian distributions which model the uncertainties of those two ASCs. The proposed distance measure can better exploit the inner discrepancy between individual ASCs compared with the Euclid distance or Mahalanobis distance. Based on the proposed distance measure, a cost matrix which contains the costs of false and missing ASCs is built and the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets. A threshold method is carried out to further evaluate the Hungarian assignment. Afterwards, a robust similarity measure is designed to evaluate the similarity between the two ASC sets which comprehensively considers the influences of the missing and false ASCs as well as the disproportionate contributions by different ASCs. Finally, the target type is determined by the similarities between the testing image and various types of template targets. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset verify the validity and robustness of the proposed method.

Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition

Abstract. This paper proposes a robust method for the matching of attributed scattering centers (ASCs) with application to synthetic aperture radar automatic target recognition (ATR). For the testing image to be classified, ASCs are extracted to match with the ones predicted by templates. First, Hungarian algorithm is employed to match those two ASC sets initially. Then, a precise matching is carried out through a threshold method. Point similarity and structure similarity are calculated, which are fused to evaluate the overall similarity of the two ASC sets based on the Dempster–Shafer theory of evidence. Finally, the target type is determined by such similarities between the testing image and various types of targets. Experiments on the moving and stationary target acquisition and recognition data verify the validity of the proposed method.

Line-scan camera calibration in close-range photogrammetry

A novel line-scan camera calibration method in close-range photogrammetry is proposed. Since the line-scan camera is only sensing in one dimension, its hard to recognize the space points from the linear data captured in static state. To address this problem, the camera is fixed to a programmable linear stage. With the help of the linear stage, a scan image of the pattern is grabbed by the line-scan camera in uniform rectilinear motion state. Therefore, the image points are definitely matched with the space points on the pattern. A pair of projective equations is established to describe this dynamic imaging model, which is determined by six extrinsic camera parameters, five intrinsic camera parameters and three other motion parameters. All the fourteen parameters are estimated approximately by using the direct linear transformation of a reasonably simplified camera model firstly, and then the results are further refined by non-linear least square mean (LSM). Both computer simulated data and real data are used to test our calibration method. The robustness and accuracy are verified by lots of simulated experiments, and for the real data, the root mean square error of re-projected points is less than 0.3 pixels.

Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers

This paper presents an approach for attributed scattering center (ASC) matching with application to synthetic aperture radar (SAR) automatic target recognition (ATR). A statistics-based distance measure is designed to evaluate the distance between individual ASCs. Afterwards, the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets. Based on the correspondence, a global similarity and a local similarity are designed to comprehensively evaluate the global consistency and structural correlation between those two ASC sets. The two similarities comprehensively exploit the inner correlation between the two ASC sets, thus providing a reliable and robust similarity measure for SAR ATR. The two similarities are then fused based on the Dempster–Shafer evidence theory to determine the target type by the maximum belief rule. Extensive experiments conducted on the moving and stationary target acquisition and recognition dataset and the comparison with several state-of-the-art methods demonstrate the validity and robustness of the proposed method.

Target recognition in synthetic aperture radar images using binary morphological operations

Abstract. Feature extraction and matching are two important steps in synthetic aperture radar automatic target recognition. This paper uses the binary target region as the feature and proposes a matching scheme for the target regions using binary morphological operations. The residuals between the testing target region and its corresponding template target regions are processed by the morphological opening operation. Then, a similarity measure is defined based on the residual remains to evaluate the similarities between different targets. Afterward, a Bayesian decision fusion is employed to fuse the similarities gained by different structuring elements to further enhance the recognition performance. The nonlinearity of the opening operation as well as the Bayesian decision fusion makes the proposed method robust to the nonlinear deformations of the target region. Experimental results on the moving and stationary target acquisition and recognition dataset demonstrate the validity of the proposed method.

Target recognition in SAR images by exploiting the azimuth sensitivity

ABSTRACT Azimuth sensitivity is a significant characteristic of synthetic aperture radar (SAR) images. Most of the previous SAR target recognition algorithms try to cope with the property by pose estimation or training classifiers which are not sensitive to azimuth. Actually, the azimuth sensitivity can provide discriminative information for target recognition as a supplement to the original spatial image (SI). This letter describes the azimuth sensitivity by the azimuth sensitivity image (ASI) which is constructed by comparing the sub-aperture images of the SI. Then the SI and ASI are classified by the sparse representation-based classification (SRC), respectively. Afterwards, a score-level fusion is employed to combine the two results for robust target recognition. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the performance is compared with several state-of-the-art methods. The experimental results show that the ASI can complement the SI for effective and robust target recognition.

Data Augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Target Recognition

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target’s image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.

Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model

Deformable-part-based model (DPM) has shown great success in object detection in recent years. However, its performance will degrade on partially occluded objects and is even worse on largely occluded objects in real remote sensing applications. To address this problem, a novel partial configuration object model (PCM) is developed in this paper. Compared to conventional single-layer DPMs, an extra partial configuration layer, which is composed of partial configurations defined according to possible occlusion patterns, is introduced in PCM to block the transmission of occlusion impact. During detection, each hypothesis from a partial configuration layer will infer the entire object based on spatial interrelationship and final detection results are obtained from the fusion of these possible entire objects using a weighted continuous clustering method. As PCM makes a better compromise between the deformation modeling flexibility of small parts and the discriminative shape-capturing capability of large DPM, its performance on occluded object detection will be improved. Moreover, occlusion states of detected objects can be inferred with the intermediate results of our model. Experimental results on multiple high-resolution remote sensing image datasets demonstrate the effectiveness of the proposed model.

A Novel Line Scan Camera Calibration Technique With an Auxiliary Frame Camera

A practical line scan camera calibration technique for close-range photogrammetric applications is proposed. It is implemented by rigidly coupling the line scan camera to an auxiliary frame camera whose intrinsic parameters have been obtained in advance. Then, the calibration is divided into two independent stages. First, images of a 2-D dynamic pattern are acquired by the two cameras from several different views. Based on these images and line scan camera model, intrinsic parameters of the line scan camera and rigid transform parameters between the two coupled cameras are calibrated. This work can be accomplished previously in workroom. Secondly, in photogrammetry, extrinsic parameters of the line scan camera are determined indirectly via space resection of the auxiliary frame camera and the obtained rigid transform parameters of the two cameras. Experiments show that our calibration can provide robust and accurate results.

Model-based Estimation for Pose, Velocity of Projectile from Stereo Linear Array Image

Model-based Estimation for Pose, Velocity of Projectile from Stereo Linear Array Image The pose (position and attitude) and velocity of in-flight projectiles have major influence on the performance and accuracy. A cost-effective method for measuring the gun-boosted projectiles is proposed. The method adopts only one linear array image collected by the stereo vision system combining a digital line-scan camera and a mirror near the muzzle. From the projectiles stereo image, the motion parameters (pose and velocity) are acquired by using a model-based optimization algorithm. The algorithm achieves optimal estimation of the parameters by matching the stereo projection of the projectile and that of the same size 3D model. The speed and the AOA (angle of attack) could also be determined subsequently. Experiments are made to test the proposed method.