Paper Info
Title
Robust Pol-ISAR Target Recognition Based on ST-MC-DCNN
Abstract
Although the deep convolutional neural network (DCNN) has been successfully applied to automatic target recognition (ATR) of ground vehicles based on synthetic aperture radar (SAR), most of the available techniques are not suitable for inverse synthetic aperture radar (ISAR) because they cannot tackle the inherent unknown deformation (e.g., translation, scaling, and rotation) among the training and test samples. To achieve robust polarimetric-ISAR (Pol-ISAR) ATR, this paper proposes the spatial transformer-multi-channel-deep convolutional neural network, i.e., ST-MC-DCNN. In this structure, we adopt the double-layer spatial transformer network (STN) module to adjust the image deformation of each polarimetric channel and then perform a robust hierarchical feature extraction by MC-DCNN. Finally, we carry out feature fusion in the concatenation layer and output the recognition result by the softmax classifier. The proposed network is end-to-end trainable and could learn the optimal deformation parameters automatically from training samples. For the fully Pol-ISAR image database generated from electromagnetic (EM) echoes of four satellites, the proposed structure achieves higher recognition accuracy than traditional DCNN and MC-DCNN. Additionally, it has shown robustness to image scaling, rotation, and combined deformation.
Year
DOI
Venue
2019
10.1109/TGRS.2019.2930112
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Feature extraction,Scattering,Strain,Target recognition,Shape,Azimuth,Image recognition
Computer vision,Inverse synthetic aperture radar,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
57
12
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xueru Bai116925.80
Xuening Zhou200.34
Feng Zhang3141.10
Li Wang425056.88
Ruihang Xue551.79
Feng Zhou6289.72