Target detection and recognition in SAR imagery based on KFDA

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

Molecular Dynamics Simulation of Persistent Slip Bands Formation in Nickel-base Superalloys

Persistent slip band （PSB） is an important and typical microstructure generated during fatigue crack initiation. Intensive work has been done to investigate the mechanisms of the formation of persistent slip bands since the 1950s when Wadsworth[1] observed the fatigue fracture in copper. Simulations have indicated that PSBs formation during fatigue crack initiation is related to the dislocation driving force and interaction. In this paper, a molecular dynamics （MD） simulation associated with embedded atom model （EAM） is applied to the PSBs formation in nickel-base superalloys with different microstructure and temperature under tensile- tensile loadings. Five MD models with different microstructure （pure 5/ phase and γ/γ＇ phase）, grain orientation （[1 0 0][0 1 0][0 0 1] and [1 1 1][1 0 1][1 2 1]） and simulation temperature （300 K, 600 K, 900 K） were built up in these simulations. Our results indicated that within the γ phase by massive dislocations, pile-up and propagation which can penetrate the grain. Also, it is found that the temperature will affect the material fatigue performance and blur PSBs appearance. The simulation results are in strong agreement with published experimental test result. This simulation is based on the work[2]. The highlights of the article include： 1） investigation of the PSB formation via molecular dynamics simulation with three different parameters, 2） conduct of a new deformation and velocity combination controlled simulation for the PSB formation, 3） high-performance computing of PSB formation, and 4） systematic analysis of the PSB formation at the atomic scale in which the dislocation plays a critical role.