Abstract
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.
Highlights
Since images from Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) outperform optical images in their adaptivity to different weather, durability in time and extensity of detection range, SAR has always been a vital interest of numerous researchers of space-based earth observation
Sparse Representation-based Classifier (SRC) [42] is a method using the Orthogonal Matching Pursuit (OMP) algorithm to solve the SAR ATR task with an accuracy of about 94.6%, while A-ConvNet [43] using the Convolutional Neural Networks (CNN) model is engaged with a better performance at about 97.5%
A deep transferred multi-level feature fusion attention network with dual optimized loss for small-sample SAR ATR tasks is proposed, which can efficiently enhance the discriminative power of feature representation learned from the proposed network
Summary
Since images from Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) outperform optical images in their adaptivity to different weather, durability in time and extensity of detection range, SAR has always been a vital interest of numerous researchers of space-based earth observation. Ma et al [18] utilized a CNN-based method to work on ship classification and detection using GF-3 SAR images. SAR target recognition depends on substantial labeled training images to train a robust model for classification, but is widely limited by space scope, region accessibility, and high-cost data acquisition. In this regard, this paper serves to mitigate the dependence on large training samples by designing a network called a multi-level feature fusion attention network that combines the feature fusion method to attach more attention to the classification mechanism. After the analysis and results of the proposed methods are unveiled in Section 4, Section 5 draws a short conclusion of the whole paper
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