NDLI: Compact convolutional autoencoder for SAR target recognition

Content Provider	IET Digital Library
Author	Guo, Jun Wang, Ling Zhu, Daiyin Hu, Changyu
Abstract	Learning discriminative features is difficult for deep learning-based target recognition in synthetic aperture radar (SAR) images with small training samples. To achieve a better feature learning, this study proposes a new deep network, a compact convolutional autoencoder (CCAE) for SAR target recognition. CCAE minimises the reconstruction loss and the distance between intra-class samples simultaneously by imposing compactness constraint on the encoder, which results in a more discriminative feature representation. Furthermore, the pretrained CCAE encoder can be used to initialise the corresponding parameters of a convolutional neural network to facilitate the training of the end-to-end model. Experimental results using the moving and stationary target acquisition and recognition dataset show that the proposed method outperforms the existing deep learning-based methods in the case of small training samples.
Starting Page	967
Ending Page	972
Page Count	6
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 7, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0447
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-01-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Compact Convolutional Autoencoder Compactness Constraint Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Deep Learning-based Method Deep Learning-based Target Recognition Deep Network Discriminative Feature Representation Electrical Engineering Computing Feature Extraction Feature Learning Image Classification Image Recognition Image Representation Intra-class Sample Knowledge Engineering Technique Learning in AI Neural Computing Technique Object Recognition Pretrained CCAE Encoder Radar Computing Radar Equipment Radar Imaging Recognition Dataset SAR Target Recognition Stationary Target Acquisition Synthetic Aperture Radar Synthetic Aperture Radar Image System And Application Training Sample
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Synthetic aperture radar target recognition of incomplete training datasets via Siamese network

Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Target recognition in synthetic aperture radar image based on PCANet

SAR ATR with full-angle data augmentation and feature polymerisation

Adaptive convolutional network for SAR image classification

Synthetic aperture radar automatic target classification processing concept

SAR-ATR method based on dual convolution capsule network

Pose-informed deep learning method for SAR ATR

MF-SarNet: Effective CNN with data augmentation for SAR automatic target recognition

Compact convolutional autoencoder for SAR target recognition

Similar Documents

Synthetic aperture radar target recognition of incomplete training datasets via Siamese network

Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Target recognition in synthetic aperture radar image based on PCANet

SAR ATR with full-angle data augmentation and feature polymerisation

Adaptive convolutional network for SAR image classification

Synthetic aperture radar automatic target classification processing concept

SAR-ATR method based on dual convolution capsule network

Pose-informed deep learning method for SAR ATR

MF-SarNet: Effective CNN with data augmentation for SAR automatic target recognition

Compact convolutional autoencoder for SAR target recognition