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.

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 Multi-Block Mixed Sample Semi-Supervised Learning for SAR Target Recognition

2021 ◽  
Vol 13 (3) ◽  
pp. 361
Author(s):  
Ye Tian ◽  
Jianguo Sun ◽  
Pengyuan Qi ◽  
Guisheng Yin ◽  
Liguo Zhang
Keyword(s):  
Supervised Learning ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Training Image ◽  
Data Sets ◽  
Data Set ◽  
Stationary Target ◽  
Mixing Method ◽  
Sar Target Recognition

In recent years, synthetic aperture radar (SAR) automatic target recognition has played a crucial role in multiple fields and has received widespread attention. Compared with optical image recognition with massive annotation data, lacking sufficient labeled images limits the performance of the SAR automatic target recognition (ATR) method based on deep learning. It is expensive and time-consuming to annotate the targets for SAR images, while it is difficult for unsupervised SAR target recognition to meet the actual needs. In this situation, we propose a semi-supervised sample mixing method for SAR target recognition, named multi-block mixed (MBM), which can effectively utilize the unlabeled samples. During the data preprocessing stage, a multi-block mixed method is used to interpolate a small part of the training image to generate new samples. Then, the new samples are used to improve the recognition accuracy of the model. To verify the effectiveness of the proposed method, experiments are carried out on the moving and stationary target acquisition and recognition (MSTAR) data set. The experimental results fully demonstrate that the proposed MBM semi-supervised learning method can effectively address the problem of annotation insufficiency in SAR data sets and can learn valuable information from unlabeled samples, thereby improving the 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.

scholarly journals Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chenyu Li ◽  
Guohua Liu
Keyword(s):  
Bayesian Learning ◽  
Block Structure ◽  
Target Recognition ◽  
Recognition Performance ◽  
Test Sample ◽  
Synthetic Aperture ◽  
Sparse Bayesian Learning ◽  
Sar Images ◽  
Stationary Target ◽  
Sar Target Recognition

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

scholarly journals Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dabin Jeong ◽  
Sangsoo Lim ◽  
Sangseon Lee ◽  
Minsik Oh ◽  
Changyun Cho ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Computational Method ◽  
Transcriptional Networks ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Data Set ◽  
Kernel Canonical Correlation Analysis ◽  
Kernel Cca

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

scholarly journals Compositional Canonical Correlation Analysis

2017 ◽  
Author(s):  
Jan Graffelman ◽  
Vera Pawlowsky-Glahn ◽  
Juan José Egozcue ◽  
Antonella Buccianti
Keyword(s):  
Trace Elements ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Covariance Matrices ◽  
Generalized Inverses ◽  
Geological Data ◽  
Data Set ◽  
X Ray ◽  
Log Ratio

AbstractThe study of the relationships between two compositions by means of canonical correlation analysis is addressed A coimnositional version of canonical correlation analysis is developed. and called CODA-CCO. We consider two approaches, using the centred log-ratio transformation and the calculation of all possible pairwise log-ratios within sets. The relationships between both approaches are pointed out, and their merits are discussed. The related covariance matrices are structurally singular, and this is efficiently dealt with by using generalized inverses. We develop compositional canonical biplots and detail their properties. The canonical biplots are shown to be powerful tools for discovering the most salient relationships between two compositions. Some guidelines for compositional canonical biplots construction are discussed. A geological data set with X-ray fluorescence spectrometry measurements on major oxides and trace elements is used to illustrate the proposed method. The relationships between an analysis based on centred log-ratios and on isometric log-ratios are also shown.

Combination of ELM and BEMD for Target Recognition of SAR Images

2021 ◽  
Vol 30 (13) ◽  
Author(s):  
Zhichao Liu ◽  
Baida Qu
Keyword(s):  
Feature Extraction ◽  
Embedded System ◽  
High Efficiency ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Sar Images ◽  
Stationary Target

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

scholarly journals A-157 How Important Is Sustained Attention in Reversal Learning and Visual Task Shifting Abilities: A Canonical Correlation Analysis in Adults

2020 ◽  
Vol 35 (6) ◽  
pp. 951-951
Author(s):  
Gracian E ◽  
Mathew A ◽  
Jimenez T ◽  
Oleson S ◽  
Kaufman D ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Reversal Learning ◽  
Sustained Attention ◽  
Canonical Correlation ◽  
Performance Test ◽  
Task Shifting ◽  
Visual Task ◽  
Data Set ◽  
And Task

Abstract Objective We used canonical correlation analysis (CCA) to examine the relationship between performance on cognitive neuroscience measures of sustained attention, deterministic reversal learning (DRLT), and visual task-shifting (VTS). We evaluated whether DRLT and VTS predicted performance on the Continuous Performance Test-II (CPT-II). Method Participants were 1011 adults from the Consortium for Neuropsychiatric Phenomics. The first CCA was conducted between four VST variables (set 1) and three CPT-II variables (set 2). The second CCA was conducted using eight Reversal Learning variables (set 1) and three CPT-II variables (set 2). Results Our first CCA suggests that accuracy of performance in VTS predicts CPT-II measures, Rc = 0.33, Wilks’s λ = 0.86, F(12, 2646) = 1.92, p < .001. The analysis revealed a positive relationship with Hits (=0.87) and a negative relationship with FA (= − 0.76), consistent with sustained attention. The second CCA revealed that acquisition trials and RT on reversal trials significantly predicted less FA and more hits on the CPT-II, Rc = 0.23, Wilks’s λ = 0.90, F(24, 1273) = 1.92, p = .005. Conclusion Our multivariate findings confirm that attention is significantly involved in executive and mnemonic processes. To our knowledge, we are the first neuroscientific group to report multivariate evidence from a large data set that confirms sustained attention plays a significant role in reversal learning and task-shifting. Our results show that the CPT-II FA and mean RT variables specifically are important predictors of reversal learning and task-shifting, strengthening the concurrent validity of our experimental measures.

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