Abstract
A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.
Highlights
Synthetic aperture radar (SAR) image processing has potential value in both military and civil fields [1]
It can be seen that the decomposition results can effectively describe the characteristics associated with the target, while forming an effective complement to the original image with more detailed information. erefore, this paper jointly uses the original image and bidimensional intrinsic mode functions (BIMF) decomposed by complex bidimensional empirical mode decomposition (C-bidimensional empirical mode decomposition (BEMD)) for the following classification
C-BEMD is an extension of traditional BEMD in the complex domain and can be directly used to process complex matrix
Summary
Synthetic aperture radar (SAR) image processing has potential value in both military and civil fields [1]. Feature extraction is one of the key steps in SAR target recognition, which mainly realizes the extraction and representation of target characteristics. At this stage, commonly used SAR image features include geometric ones, transformation ones, and electromagnetic ones. A large number of classifiers have been used and verified in SAR target recognition, including support vector machines (SVM) [32, 33] and sparse representation-based classification (SRC) [34,35,36]. E sole use of image intensities would lose the discrimination of the phase distribution In this sense, C-BEMD can more effectively reflect the two-dimensional time-frequency characteristics of the target, thereby providing more sufficient information for the following classification. E experimental results verify the effectiveness and robustness of this method
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