An automatic target recognition algorithm for SAR image based on improved convolution neural network

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11020135 ◽

2019 ◽

Vol 11 (2) ◽

pp. 135 ◽

Cited By ~ 5

Author(s):

Xiaoran Shi ◽

Feng Zhou ◽

Shuang Yang ◽

Zijing Zhang ◽

Tao Su

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Target Recognition ◽

Super Resolution ◽

Automatic Target Recognition ◽

Synthetic Aperture ◽

Sar Image ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Feature Characterization

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

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Automatic target recognition using a multilayer convolution neural network

10.1117/12.243153 ◽

1996 ◽

Cited By ~ 4

Author(s):

Vincent Mirelli ◽

Syed A. Rizvi

Keyword(s):

Neural Network ◽

Target Recognition ◽

Automatic Target Recognition ◽

Convolution Neural Network

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SAR Image Target Recognition Algorithm Based On Convolutional Neural Network

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) ◽

10.1109/aiid51893.2021.9456459 ◽

2021 ◽

Author(s):

Bodi Feng ◽

Haitao Yang ◽

Changgong Zhang ◽

Jingyu Wang ◽

Gaoyuan Li ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Target Recognition ◽

Recognition Algorithm ◽

Sar Image

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A Novel SAR Image Target Recognition Algorithm under Big Data Analysis

Wireless Communications and Mobile Computing ◽

10.1155/2021/4556157 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xiang Chen ◽

Xing Wang ◽

You Chen ◽

Haihan Wang

Keyword(s):

Big Data ◽

Data Analysis ◽

Target Recognition ◽

Image Interpretation ◽

Image Data ◽

Automatic Target Recognition ◽

Big Data Analysis ◽

Recognition Algorithm ◽

Sar Image ◽

Data Set

Synthetic aperture radar (SAR) image target recognition technology is aimed at automatically determining the presence or absence of target information from the input SAR image and improving the efficiency and accuracy of SAR image interpretation. Based on big data analysis, dirty data is removed, clean data is returned, and standardized processing of SAR image data is realized. At the same time, by establishing a statistical model of coherent speckles, the convolutional autoencoder is used to denoise the SAR image. Finally, the network model modified by softmax cross-entropy loss and Fisher loss is used for automatic target recognition. Based on the MSTAR data set, two scene graphs containing the target synthesized by the background image and the target slice are used for experiments. Several comparative experiments have verified the effectiveness of the classification and recognition model in this paper.

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