Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized...
Main Authors: | Xinzheng Zhang, Yijian Wang, Zhiying Tan, Dong Li, Shujun Liu, Tao Wang, Yongming Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-11-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/17/11/2506 |
Similar Items
-
Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification
by: Xinzheng Zhang, et al.
Published: (2018-09-01) -
Synthetic Aperture Radar Target Recognition Using Weighted Multi-Task Kernel Sparse Representation
by: Chen Ning, et al.
Published: (2019-01-01) -
Multitask Learning for Ship Detection From Synthetic Aperture Radar Images
by: Xin Zhang, et al.
Published: (2021-01-01) -
Person Re-Identification by Weighted Integration of Sparse and Collaborative Representation
by: Jie Guo, et al.
Published: (2017-01-01) -
SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal
by: Meiting Yu, et al.
Published: (2019-11-01)