Abstract
Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.
Highlights
The development of synthetic aperture radar (SAR) technology has witnessed the explosive growth of available SAR images
We have proposed a feature fusion method based on stacked autoencoder (SAE) for SAR automatic target recognition
Baseline features and Three-Patch Local Binary Pattern (TPLBP) features are fused in a well-designed SAE and further fed to a softmax classifier for recognition
Summary
The development of synthetic aperture radar (SAR) technology has witnessed the explosive growth of available SAR images. Manual interpretation of numerous SAR images is time-consuming and almost impractical. This has significantly accelerated the development of automatic target recognition (ATR) algorithms. Choosing efficient features is important for traditional SAR ATR techniques and many feature extraction methods have been developed to describe the targets in SAR images [1,2,3,4,5]. The feature fusion algorithms are mainly divided into three categories [6]: The first category is a method of feature combination, combining the features in series or in parallel according to certain weights for obtaining a new feature vector [7]. The third category is feature transformation, which is a way to convert raw features into new feature representations [10,11]
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