Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks

Jun Wang; Yuming Ren; Shaoming Wei

doi:10.1109/ICSIDP47821.2019.9172989

Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks

Jun Wang
, Yuming Ren
, Shaoming Wei

School of Electronic and Information Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In order to improve the precision of target detection and recognition for synthetic aperture radar (SAR) images, in this paper, we proposed the multiscale feature extraction and fusion method for SAR images based on the convolutional neural networks. We constructed training and testing data based on the MSTAR dataset. Since there are not enough SAR image data, we used image processing methods to do the data augmentation. In order to improve the accuracy of target detection, we also used the method of transfer learning. Eventually we trained and tested the model on a small data set, the final mAP reached 96.58%, a relatively high score which proved the effectiveness of multiscale feature extraction and fusion. In order to better understand the principle of this technology, we also did some visualization analysis for the feature maps. This proved the reliability of the method.

Original language	English
Title of host publication	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728123455
DOIs	https://doi.org/10.1109/ICSIDP47821.2019.9172989
State	Published - Dec 2019
Event	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China Duration: 11 Dec 2019 → 13 Dec 2019

Publication series

Name	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/Territory	China
City	Chongqing
Period	11/12/19 → 13/12/19

Keywords

convolutional neural networks
deep learning
feature visualization
multiscale feature extraction and fusion
synthetic aperture radar
target detection

Access to Document

10.1109/ICSIDP47821.2019.9172989

Cite this

Wang, J., Ren, Y., & Wei, S. (2019). Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019 Article 9172989 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIDP47821.2019.9172989

Wang, Jun ; Ren, Yuming ; Wei, Shaoming. / Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks. ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019).

@inproceedings{893be734f38e4b65b679398f217bb4a4,

title = "Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks",

abstract = "In order to improve the precision of target detection and recognition for synthetic aperture radar (SAR) images, in this paper, we proposed the multiscale feature extraction and fusion method for SAR images based on the convolutional neural networks. We constructed training and testing data based on the MSTAR dataset. Since there are not enough SAR image data, we used image processing methods to do the data augmentation. In order to improve the accuracy of target detection, we also used the method of transfer learning. Eventually we trained and tested the model on a small data set, the final mAP reached 96.58\%, a relatively high score which proved the effectiveness of multiscale feature extraction and fusion. In order to better understand the principle of this technology, we also did some visualization analysis for the feature maps. This proved the reliability of the method.",

keywords = "convolutional neural networks, deep learning, feature visualization, multiscale feature extraction and fusion, synthetic aperture radar, target detection",

author = "Jun Wang and Yuming Ren and Shaoming Wei",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ICSIDP47821.2019.9172989",

language = "英语",

series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",

address = "美国",

}

Wang, J, Ren, Y & Wei, S 2019, Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks. in ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019., 9172989, ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019, Chongqing, China, 11/12/19. https://doi.org/10.1109/ICSIDP47821.2019.9172989

Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks. / Wang, Jun; Ren, Yuming; Wei, Shaoming.
ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 9172989 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks

AU - Wang, Jun

AU - Ren, Yuming

AU - Wei, Shaoming

PY - 2019/12

Y1 - 2019/12

N2 - In order to improve the precision of target detection and recognition for synthetic aperture radar (SAR) images, in this paper, we proposed the multiscale feature extraction and fusion method for SAR images based on the convolutional neural networks. We constructed training and testing data based on the MSTAR dataset. Since there are not enough SAR image data, we used image processing methods to do the data augmentation. In order to improve the accuracy of target detection, we also used the method of transfer learning. Eventually we trained and tested the model on a small data set, the final mAP reached 96.58%, a relatively high score which proved the effectiveness of multiscale feature extraction and fusion. In order to better understand the principle of this technology, we also did some visualization analysis for the feature maps. This proved the reliability of the method.

AB - In order to improve the precision of target detection and recognition for synthetic aperture radar (SAR) images, in this paper, we proposed the multiscale feature extraction and fusion method for SAR images based on the convolutional neural networks. We constructed training and testing data based on the MSTAR dataset. Since there are not enough SAR image data, we used image processing methods to do the data augmentation. In order to improve the accuracy of target detection, we also used the method of transfer learning. Eventually we trained and tested the model on a small data set, the final mAP reached 96.58%, a relatively high score which proved the effectiveness of multiscale feature extraction and fusion. In order to better understand the principle of this technology, we also did some visualization analysis for the feature maps. This proved the reliability of the method.

KW - convolutional neural networks

KW - deep learning

KW - feature visualization

KW - multiscale feature extraction and fusion

KW - synthetic aperture radar

KW - target detection

UR - https://www.scopus.com/pages/publications/85091905062

U2 - 10.1109/ICSIDP47821.2019.9172989

DO - 10.1109/ICSIDP47821.2019.9172989

M3 - 会议稿件

AN - SCOPUS:85091905062

T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

Y2 - 11 December 2019 through 13 December 2019

ER -

Wang J, Ren Y, Wei S. Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 9172989. (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). doi: 10.1109/ICSIDP47821.2019.9172989

Synthetic Aperture Radar Images Target Detection and Recognition with Multiscale Feature Extraction and Fusion Based on Convolutional Neural Networks

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