With the development of the remote sensing technology, the availability of satellite images has been dramatically increased with high quantity and quality. Diverse information can be obtained from these multiple imaging sources. For example, synthetic aperture radar (SAR) imagery measures physical properties of the observed scene in all-weather and full-time situation and follows a range-based imaging geometry, while optical imagery measures chemical characteristics of the scene and follows a perspective imaging geometry and needs both daylight and a cloudless sky. These multisource remote sensing images, once fused together, provide a more comprehensive interpretation of remote sensing scenes. Recent advances in Generative adversarial networks (GANs) have shown great promise in translating imagery between modalities, as well in the generation of high resolution and realistic imagery. In this paper, a GAN architecture is used to solve the task of fusing SAR and optical remote sensing imagery. The network learns the mapping between input and output image, and learns a loss function to train this mapping. Specifically, the generated network is divided into two parts, encoding and decoding. The fused image including SAR intensity and texture information is generated by the generator. Other details of the optical image are added to the fusion image gradually by the discriminator. The structural similarity loss function of GAN is to make the training of GAN model more accurate on the whole structure. Experiments on Sentinel-1and Sentinel-2 imagery confirm the effectiveness and efficiency of the proposed method.
This paper proposes an absolute attitude measurement approach by utilizing a monostatic wideband radar. In this approach, the three-dimensional electromagnetic-model (3-D em-model) and the parametric motion model of a target are combined to estimate absolute attitude. The 3-D em-model is established offline based on the target’s geometric structure. Scattering characteristics such as radar cross section and radar images from one-dimension to 3-D can be conveniently predicted by this model. By matching the high-resolution range profiles (HRRPs) of measurements with the HRRPs predicted by the 3-D em-model, the directions of the lines of sight relative to the target at different measuring times are first obtained. Then, based on the obtained directions and the parametric motion model of the target, the target absolute attitude at each measuring time can be acquired. Experiments using both data predicted by a high-frequency em-code and data measured in an anechoic chamber verify the validity of the proposed method.
Electromagnetic model (em-model) provides a concise and physically relevant description of target through representative scatterers. In a forward built em-model, detailed information about each scatterer’s position, scattering amplitude along with its provenance can be predicted. This makes em-model a good candidate for use in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we introduce scatterers’ provenance as attributed information into target recognition, and an attributed em-model based target recognition method is proposed. Firstly, according to the purpose of ATR, each scatterer in em-model is endowed with an importance factor based on its provenance. Secondly, a detection is implemented to decide whether the em-model predicted scatterer has a corresponding scatterer in measured data. If the scatterer exist in measured target, evaluate how similar the scatterer pair resembled with each other. Next, similarities of all the scatterer pairs are synthesized as a whole match score between em-model and SAR data. In the synthesis, the importance factor servers as a weighting factor that scatterer with more attention will be more discriminative for recognition. In the end, target in measured SAR data is recognized as the model type or not based on the match score. The novelty of this method comes from taking into account of the provenance information of scatterers as attributed information and endowing the scatterers with different important factors according to their importance in recognition. This makes the attributed scatterer based recognition method pertinent to the purpose of ATR. Experiments on simulated Tank SAR data that produced by a high frequency electromagnetic simulation software verified the effectiveness of this method.
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.
A three-dimensional electromagnetic model (3-D EM-model)–based scattering center matching method is developed for synthetic aperture radar automatic target recognition (ATR). 3-D EM-model provides a concise and physically relevant description of the target’s electromagnetic scattering phenomenon through its scattering centers which makes it an ideal candidate for ATR. In our method, scatters of the 3-D EM-model are projected to the two-dimensional measurement plane to predict scatters’ location and scattering intensity properties. Then the identical information is extracted for scatters in measured data. A two-stage iterative operation is applied to match the model-predicted scatters and the measured data-extracted scatters by combining spatial and attributed information. Based on the two scatter sets’ matching information, a similarity measurement between model and measured data is obtained and recognition conclusion is made. Meanwhile, the target’s configuration is reasoned with 3-D EM-model serving as a reference. In the end, data simulated by electromagnetic computation verified this method’s validity.
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.
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