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 acquisition and further insight into the intuitive features of SAR images are the main concerns. In this paper, a deep transferred multi-level feature fusion attention network with dual optimized loss, called a multi-level feature attention Synthetic Aperture Radar network (MFFA-SARNET), is proposed to settle the problem of small samples in SAR ATR tasks. Firstly, a multi-level feature attention (MFFA) network is established to learn more discriminative features from SAR images with a fusion method, followed by alleviating the impact of background features on images with the following attention module that focuses more on the target features. Secondly, a novel dual optimized loss is incorporated to further optimize the classification network, which enhances the robust and discriminative learning power of features. Thirdly, transfer learning is utilized to validate the variances and small-sample classification tasks. Extensive experiments conducted on a public database with three different configurations consistently demonstrate the effectiveness of our proposed network, and the significant improvements yielded to surpass those of the state-of-the-art methods under small-sample conditions.
MFFA-SARNET : Deep Transferred Multi-level Feature Fusion Attention Network for Small Samples SAR ATR with Dual Optimized Loss / Y. Zhai, W. Deng, B. Sun, T. Li, B. Sun, Z. Ying, J.G.a.C. Mai, J. Li, R. Donida Labati, V. Piuri, F. Scotti. - In: REMOTE SENSING. - ISSN 2072-4292. - 12:9(2020 May), pp. 1385.1-1385.20.
|Titolo:||MFFA-SARNET : Deep Transferred Multi-level Feature Fusion Attention Network for Small Samples SAR ATR with Dual Optimized Loss|
|Parole Chiave:||Attention network; Dual optimized loss; Feature fusion; SAR ATR; Small samples; Transfer learning|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||mag-2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/RS12091385|
|Appare nelle tipologie:||01 - Articolo su periodico|