This paper presents an Automatic Target Recognition (ATR) system for laser radar (LADAR) imagery, designed to classify objects at multiple levels of discrimination (target detection, classification, and recognition) from single LADAR images. Segmentation is performed in both the range and non-range LADAR channels and results combined to increase object detection rate or decrease false positive ...

In this study, a target classification method based on point cloud data in high-resolution radar sensor is proposed. By using multiple antenna elements arranged horizontal and vertical directions, pedestrians, cyclists vehicles can be expressed as the three-dimensional (3D) space. To perform spatial characteristics (i.e. length, height width) of target, 3D orthogonally projected onto xy, yz zx ...

Two fusion strategies for target recognition using multiaspect synthetic aperture radar (SAR) images are presented for recognizing ground vehicles in MSTAR database. Due to radar crosssection variability, the ability to discriminate between targets varies greatly with target aspect. Multi-aspect images of a given target are used to support recognition. In this paper, two fusion strategies for t...

The scattering properties of a radar target can only be fully characterized by measuring a scattering matrix in which the elements represent the combinations of transmit and receive polarization states. The amount of information gathered by a traditional single polarization radar may therefore be even very limited, for example when the single polarization response is near zero while the cross p...

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and o...

There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar sy...

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level capabilities of military civilian automation. Although RATR has been successfully applied some aspects, complete theoretical not established. At present, deep le...

While radar search and target detection are essential to radar use, most applications also require measurement of target characteristics. These may include target position and velocity, as described in Sections 8.1 through 8.3, and indicators of target size, shape and rotation, as described in Section 8.4. The use of multiple radar to improve measurement accuracy is discussed in Section 8.5. Co...

In previous work we have developed a methodology for texture recognition and synthesis that estimates and exploits the dependencies across scale that occur within images.1,2 In this paper we discuss the application of this technique to synthetic aperture radar (SAR) vehicle classification. Our approachmeasures characteristic cross-scale dependencies in training imagery; targets are recognized w...