Joint super-resolution moving target feature extraction and stationary clutter suppression

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

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Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination

Remote Sensing ◽

10.3390/rs13152901 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2901

Author(s):

Zhiqiang Zeng ◽

Jinping Sun ◽

Congan Xu ◽

Haiyang Wang

Keyword(s):

Feature Extraction ◽

Position Angle ◽

Automatic Target Recognition ◽

Identification Accuracy ◽

Training Dataset ◽

Target Feature ◽

Practical Applications ◽

Feature Vectors ◽

Identification Method ◽

Leibler Divergence

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

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Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

10.21203/rs.3.rs-545193/v1 ◽

2021 ◽

Author(s):

Taiping Mo ◽

Dehong Chen

Keyword(s):

Feature Extraction ◽

High Frequency ◽

Interpolation Method ◽

Super Resolution ◽

Frequency Information ◽

Ill Posed ◽

Image Edge ◽

Image Super Resolution ◽

Improved Model ◽

Image Details

Abstract The Invertible Rescaling Net (IRN) is modeling image downscaling and upscaling as a unified task to alleviate the ill-posed problem in the super-resolution task. However, the ability of potential variables of the model embedded high-frequency information is general, which affects the performance of the reconstructed image. In order to improve the ability of embedding high-frequency information and further reduce the complexity of the model, the potential variables and feature extraction of key components of IRN are improved. Attention mechanism and dilated convolution are used to improve the feature extraction block, reduce the parameters of feature extraction block, and allocate more attention to the image details. The high frequency sub-band interpolation method of wavelet domain is used to improve the potential variables, process and save the image edge, and enhance the ability of embedding high frequency information. Experimental results show that compared with IRN model, improved model has less complexity and excellent performance.

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Research on the Bus Passenger Motion Vector Algorithm Based on Video Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.623.149 ◽

2014 ◽

Vol 623 ◽

pp. 149-155

Author(s):

Huai Kun Xiang ◽

Lan Qiu Cheng

Keyword(s):

Motion Vector ◽

Detection Algorithm ◽

Moving Target ◽

Statistical Accuracy ◽

Target Feature ◽

Complex State ◽

Image Preprocessing ◽

The Core ◽

Multi Target Tracking ◽

Density Clustering

Video-based bus passenger collection is considered to have a greater potential for counting passengers, but the difficulty lies in how to solve the problem of accurately tracking and determining the direction of the moving target under the complex state of motion. The traditional target detection algorithm, like frame subtraction and edge detection, is very difficult to deal with the situation of immediate multi-objective. Based on image preprocessing, an algorithm about intelligent video multi-target tracking and traffic statistics is designed aiming at the above-mentioned situation. The core contents in this paper includes: the grid-density clustering and line segment clustering, extraction and segmentation target connected domain that is handled to be a circular, tracking the motion vector of target feature point, finally ,achieving accurate statistics on the number of people getting on and off. Experiments show that this algorithm can effectively track more people simultaneously and the bus traffic statistical accuracy achieves the goal.

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