Geometric Algebra Neuron for SAR Automation Target Recognition

2011 ◽  
Vol 187 ◽  
pp. 319-325
Author(s):  
Wen Ming Cao ◽  
Xiong Feng Li ◽  
Li Juan Pu
Keyword(s):  
Geometric Algebra ◽  
Target Recognition ◽  
Dimensional Space ◽  
Feature Space ◽  
Automatic Target Recognition ◽  
High Dimensional ◽  
Small Data ◽  
Data Set ◽  
High Dimensional Space ◽  
Stationary Target

Biometric Pattern Recognition aim at finding the best coverage of per kind of sample’s distribution in the feature space. This paper employed geometric algebra to determine local continuum (connected) direction and connected path of same kind of target of SAR images of the complex geometrical body in high dimensional space. We researched the property of the GA Neuron of the coverage body in high dimensional space and studied a kind of SAR ATR(SAR automatic target recognition) technique which works with small data amount and result to high recognizing rate. Finally, we verified our algorithm with MSTAR (Moving and Stationary Target Acquisition and Recognition) [1] data set.

scholarly journals A Novel Convolutional Neural Network Architecture for SAR Target Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yinjie Xie ◽  
Wenxin Dai ◽  
Zhenxin Hu ◽  
Yijing Liu ◽  
Chuan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Neural Network Architecture ◽  
Data Set ◽  
Stationary Target ◽  
Optical Images

Among many improved convolutional neural network (CNN) architectures in the optical image classification, only a few were applied in synthetic aperture radar (SAR) automatic target recognition (ATR). One main reason is that direct transfer of these advanced architectures for the optical images to the SAR images easily yields overfitting due to its limited data set and less features relative to the optical images. Thus, based on the characteristics of the SAR image, we proposed a novel deep convolutional neural network architecture named umbrella. Its framework consists of two alternate CNN-layer blocks. One block is a fusion of six 3-layer paths, which is used to extract diverse level features from different convolution layers. The other block is composed of convolution layers and pooling layers are mainly utilized to reduce dimensions and extract hierarchical feature information. The combination of the two blocks could extract rich features from different spatial scale and simultaneously alleviate overfitting. The performance of the umbrella model was validated by the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set. This architecture could achieve higher than 99% accuracy for the classification of 10-class targets and higher than 96% accuracy for the classification of 8 variants of the T72 tank, even in the case of diverse positions located by targets. The accuracy of our umbrella is superior to the current networks applied in the classification of MSTAR. The result shows that the umbrella architecture possesses a very robust generalization capability and will be potential for SAR-ART.

An Error Minimizing Partitioning Method for the Nearest Neighbor Search

2013 ◽  
Vol 321-324 ◽  
pp. 2165-2170
Author(s):  
Seung Hoon Lee ◽  
Jaek Wang Kim ◽  
Jae Dong Lee ◽  
Jee Hyong Lee
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Computational Cost ◽  
Nearest Neighbor Search ◽  
High Dimensional ◽  
Index Structures ◽  
Cost Index ◽  
Data Set ◽  
High Dimensional Space ◽  
Neighbor Search

The nearest neighbor search in high-dimensional space is an important operation in many applications, such as data mining and multimedia databases. Evaluating similarity in high-dimensional space requires high computational cost; index-structures are frequently used for reducing computational cost. Most of these index-structures are built by partitioning the data set. However, the partitioning approaches potentially have the problem of failing to find the nearest neighbor that is caused by partitions. In this paper, we propose the Error Minimizing Partitioning (EMP) method with a novel tree structure that minimizes the failures of finding the nearest neighbors. EMP divides the data into subsets with considering the distribution of data sets. For partitioning a data set, the proposed method finds the line that minimizes the summation of distance to data points. The method then finds the median of the data set. Finally, our proposed method determines the partitioning hyper-plane that passes the median and is perpendicular to the line. We also make a comparative study between existing methods and the proposed method to verify the effectiveness of our method.

scholarly journals Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

2021 ◽  
Vol 13 (18) ◽  
pp. 3554
Author(s):  
Xiaowei Hu ◽  
Weike Feng ◽  
Yiduo Guo ◽  
Qiang Wang
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Feature Space ◽  
Automatic Target Recognition ◽  
Data Sets ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Public Data ◽  
Class Labels

Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.

An Identification Method to Detect the Movement of Smoke in Video

2014 ◽  
Vol 598 ◽  
pp. 432-435
Author(s):  
Shi Jiao Zhu ◽  
Cheng Jian Liu ◽  
Qing Wang
Keyword(s):  
Dimensional Space ◽  
Motion Vector ◽  
Feature Space ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Identification Method ◽  
Depth Study

In this paper, a method is proposed using the motion vector in Gabor space to recognize smoke region. Smoke orbit is described as up-forward and has some similar sharp in feature space. The propose method is assessed by calculating 45,90,135 degree upward vectors , and determined the possibility of smoke region. Different images were tested using smoke video scenes, and it meets the desired expectation. The next step will be in-depth study represents a high-dimensional space .

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 Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Ashkan Faghiri ◽  
Eswar Damaraju ◽  
Aysenil Belger ◽  
Judith M. Ford ◽  
Daniel Mathalon ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensional Space ◽  
Real Data ◽  
High Dimensional ◽  
Data Set ◽  
High Dimensional Space ◽  
New Approach ◽  
Oscillatory Pattern ◽  
Functional Dynamics ◽  
Novel Approach

BackgroundA number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.MethodsWe introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.ResultsWe present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.ConclusionOur proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

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