A correlation analysis of the Naval Surface Warfare Center Panama City Division's (NSWC PCD) database of simulated and collected target scattering responses focused on automated target recognition

2014 ◽  
Vol 136 (4) ◽  
pp. 2112-2112
Author(s):  
Raymond Lim ◽  
David E. Malphurs ◽  
James L. Prater ◽  
Kwang H. Lee ◽  
Gary S. Sammelmann
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Panama City ◽  
Naval Surface Warfare

scholarly journals A Multiview SAR Target Recognition Method Using Inner Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lei Lei ◽  
Dongen Guo ◽  
Zhihui Feng
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Image Correlation ◽  
Multiple Views ◽  
Recognition Method ◽  
Sar Images ◽  
Weighting Method ◽  
Multiview Images ◽  
Correlation Information ◽  
Sar Target Recognition

This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.

scholarly journals Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Chen ◽  
Peng Zhan ◽  
Luhui Cao ◽  
Xueqing Li
Keyword(s):  
Correlation Analysis ◽  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Support Vector ◽  
Sar Images ◽  
Stationary Target ◽  
Weighting Strategy ◽  
Set Up ◽  
Sar Target Recognition

A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

scholarly journals Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

2019 ◽  
Vol 37 (1) ◽  
pp. 87-92
Author(s):  
Honghui Yang ◽  
Shuzhen Yi
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Target Recognition ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Algorithm ◽  
Fusion Method ◽  
Underwater Target

To solve high-dimensional and small-sample-size classification problem for underwater target recognition, a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multi-domain features, remove redundant and noise features, in order to achieve multi-domain feature fusion. The multi-kernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multi-domain feature samples, which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.

scholarly journals Target Recognition in SAR Images Based on Multiresolution Representations with 2D Canonical Correlation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaojing Tan ◽  
Ming Zou ◽  
Xiqin He
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Operating Conditions ◽  
Data Set ◽  
Stationary Target ◽  
Sar Target Recognition

This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

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