MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention Network with Dual Optimized Loss for Small-Sample SAR ATR

MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention Network with Dual Optimized Loss for Small-Sample SAR ATR

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), most algorithms of which have employed and relied on sufficient training samples to receive a strong discriminative classification model, has remained a challenging task in recent years, among which the challenge of SAR data acquisit...

Full description

Bibliographic Details
Main Authors: Yikui Zhai, Wenbo Deng, Tian Lan, Bing Sun, Zilu Ying, Junying Gan, Chaoyun Mai, Jingwen Li, Ruggero Donida Labati, Vincenzo Piuri, Fabio Scotti
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
SAR ATR
attention network
feature fusion
dual optimized loss
transfer learning
small samples
Online Access:https://www.mdpi.com/2072-4292/12/9/1385

Internet

https://www.mdpi.com/2072-4292/12/9/1385

Similar Items