scholarly journals Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation

2018 ◽  
Author(s):  
Ganggang Dong ◽  
Jocelyn Chanussot
Keyword(s):  
Target Recognition ◽  
Incidence Angle ◽  
Recognition Performance ◽  
Computational Cost ◽  
Geometric Distortion ◽  
Linear Regression Models ◽  
Global Features ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Radar Images

This paper considers target characterization and recognition in radar images with keypoint-based local descriptor. Most of the preceding works rely on the global features or raw intensity values, and hence produce the limited recognition performance. Moreover, the global features are sensitive to the real-world sources of variability, such as aspect view, configu-ration, and incidence angle changes, clutter, articulation, and occlusion. Keypoint-based local descriptor was developed as a powerful strategy to address invariance to contrast change and geometric distortion. This property inspires us to investigate whether the family of local features are relevant for radar target recognition. Most of the preceding works typically devote to finding the correspondences between a collected image and a reference one. The representative applications include image register and change detection. Little work was pursued to target recognition in SAR images. This is because the huge number of local descriptors resulting from radar images make the computational cost and memory consumption unacceptable. To handle the problems, this paper develops two families of methods. The proposed methods are used to achieve target recognition by means of local descriptors. Our first solver refers to building multiple linear regression models, and addresses the problem by the theory of sparse representation. The second scheme rebuilds a new feature by the feature quantization skill, from which the inference can be drawn. Multiple comparative studies are pursued to verify the performance of detectors and descriptors popularly used. The source code was publicly released on https://ganggangdong.github.io/homepage/.

scholarly journals Multivariate Predictors of Music Perception and Appraisal by Adult Cochlear Implant Users

2008 ◽  
Vol 19 (02) ◽  
pp. 120-134 ◽  
Author(s):  
Kate Gfeller ◽  
Jacob Oleson ◽  
John F. Knutson ◽  
Patrick Breheny ◽  
Virginia Driscoll ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Music Perception ◽  
Life Experience ◽  
Recognition Performance ◽  
Strong Predictor ◽  
Music Listening ◽  
Linear Regression Models ◽  
Hearing Aid Use ◽  
Music Appraisal

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music. La investigación examinó si el desempeño, por parte de adultos receptores de un implante coclear, sobre una variedad de pruebas de reconocimiento y evaluación derivadas de la música del mundo real, podrían predecirse a partir de variables tecnológicas, demográficas y de experiencias de vida, así como de puntajes de reconocimiento del lenguaje. Participó una muestra representativa de 209 adultos implantados entre 1965 y el 2006. Usando múltiples modelos de regresión lineal y modelos mixtos lineales generalizados, se seleccionaron grupos de variables óptimas de predicción, que pudieran predecir efectivamente el desempeño por medio de una batería de pruebas que permitiera evaluar diferentes aspectos de la apreciación musical. Estos análisis establecieron la importancia de distinguir entre la exactitud en la percepción musical y la evaluación de estímulos musicales cuando se utiliza la apreciación musical como un índice de éxito en la implantación. Importantemente, ningún tipo de dispositivo o estrategia de procesamiento predijo la percepción o la evaluación musical. El desempeño en el reconocimiento del lenguaje no fue un elemento fuerte de predicción, y llegó a predecir primariamente la percepción musical cuando los estímulos de prueba incluyeron las letras. Adicionalmente, las limitaciones en la utilidad de la percepción del lenguaje a la hora de predecir la percepción y la evaluación musical, subrayan la utilidad de la percepción de la música como una medida alternativa de resultado para evaluar la implantación coclear. La música de fondo, la audición residual (p.e., el uso de auxiliares auditivos), los factores cognitivos, y algunos factores demográficos predijeron varios índices de exactitud y evaluación perceptual de la música.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

Image Quality and Target Recognition

1967 ◽  
Vol 9 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Corwin A. Bennett ◽  
Samuel H. Winterstein ◽  
Robert E. Kent
Keyword(s):  
Grain Size ◽  
Image Quality ◽  
Modulation Transfer Function ◽  
Target Recognition ◽  
Recognition Performance ◽  
Aerial Photographs ◽  
Modulation Transfer ◽  
Task Definition ◽  
Subject Selection ◽  
Target Characteristics

The terminology and literature in the area of image quality and target recognition are reviewed. An experiment in which subjects recognized strategic and tactical targets in aerial photographs with controlled image degradations is described. Some findings are: Recognition performance is only moderate for representative conditions. There are wide differences among target types in the recognizability. Knowledge of a target's presence (briefing) greatly aids recognition. Better resolution means better performance. Enlarging the image such that a line of resolution subtends more than three minutes of arc hinders recognition. Grain size should be kept below 20 seconds of arc. It is suggested that the eventual application of the modulation transfer function approach to measurement of image quality and target characteristics will enable a quantitative subsuming of various quality-size relationships. More attention needs to be paid in recognition research to suitable task definition, target description, and subject selection.

Target recognition performance following whole-views, part-views, and both-vlews training

2017 ◽  
pp. 135-141
Author(s):  
Sehchang Hah ◽  
Deborah A. Reisweber ◽  
Jose A. Picart ◽  
Harry Zwick
Keyword(s):  
Target Recognition ◽  
Recognition Performance

scholarly journals TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1724
Author(s):  
Zilu Ying ◽  
Chen Xuan ◽  
Yikui Zhai ◽  
Bing Sun ◽  
Jingwen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Network Architecture ◽  
Learning Strategy ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Speckle Noise ◽  
Small Sample ◽  
Operating Conditions ◽  
Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

A Critical Analysis on Low-Order Simulation Models for Darrieus VAWTs: How Much Do They Pertain to the Real Flow?

2018 ◽  
Author(s):  
Alessandro Bianchini ◽  
Francesco Balduzzi ◽  
Giovanni Ferrara ◽  
Giacomo Persico ◽  
Vincenzo Dossena ◽  
...  
Keyword(s):  
Flow Field ◽  
Incidence Angle ◽  
Computational Cost ◽  
Simulation Models ◽  
Small Scale ◽  
Critical Issues ◽  
Overall Performance ◽  
Lift And Drag ◽  
Real Flow ◽  
Low Order

To improve the efficiency of Darrieus wind turbines, which still lacks from that of horizontal-axis rotors, Computational Fluid Dynamics (CFD) techniques are now extensively applied, since they only provide a detailed and comprehensive flow representation. Their computational cost makes them, however, still prohibitive for routine application in the industrial context, which still makes large use of low-order simulation models like the Blade Element Momentum (BEM) theory. These models have been shown to provide relatively accurate estimations of the overall turbine performance; conversely, the description of the flow field suffers from the strong approximations introduced in the modelling of the flow physics. In the present study, the effectiveness of the simplified BEM approach was critically benchmarked against a comprehensive description of the flow field past the rotating blades coming from the combination of a two-dimensional unsteady CFD model and experimental wind tunnel tests; for both data sets, the overall performance and the wake characteristics on the mid plane of a small-scale H-shaped Darrieus turbine were available. Upon examination of the flow field, the validity of the ubiquitous use of induction factors is discussed, together with the resulting velocity profiles upstream and downstream the rotor. Particular attention is paid on the actual flow conditions (i.e. incidence angle and relative speed) experienced by the airfoils in motion at different azimuthal angles, for which a new procedure for the post-processing of CFD data is here proposed. Based on this model, the actual lift and drag coefficients produced by the airfoils in motion are analyzed and discussed, with particular focus on dynamic stall. The analysis highlights the main critical issues and flaws of the low-order BEM approach, but also sheds new light on the physical reasons why the overall performance prediction of these models is often acceptable for a first-design analysis.

scholarly journals Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1181
Author(s):  
Keisuke Yoneda ◽  
Akisuke Kuramoto ◽  
Naoki Suganuma ◽  
Toru Asaka ◽  
Mohammad Aldibaja ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Information ◽  
Recognition Performance ◽  
Computational Cost ◽  
Region Of Interest ◽  
Traffic Light ◽  
Automated Driving ◽  
Digital Map ◽  
Traffic Lights

Traffic light recognition is an indispensable elemental technology for automated driving in urban areas. In this study, we propose an algorithm that recognizes traffic lights and arrow lights by image processing using the digital map and precise vehicle pose which is estimated by a localization module. The use of a digital map allows the determination of a region-of-interest in an image to reduce the computational cost and false detection. In addition, this study develops an algorithm to recognize arrow lights using relative positions of traffic lights, and the arrow light is used as prior spatial information. This allows for the recognition of distant arrow lights that are difficult for humans to see clearly. Experiments were conducted to evaluate the recognition performance of the proposed method and to verify if it matches the performance required for automated driving. Quantitative evaluations indicate that the proposed method achieved 91.8% and 56.7% of the average f-value for traffic lights and arrow lights, respectively. It was confirmed that the arrow-light detection could recognize small arrow objects even if their size was smaller than 10 pixels. The verification experiments indicate that the performance of the proposed method meets the necessary requirements for smooth acceleration or deceleration at intersections in automated driving.

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