Abstract
It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real‐time monitoring. Sparse representation‐based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.
Highlights
As a new kind of reconnaissance remote sensing device, SAR is widely used in aerial and space reconnaissance, monitoring, and intelligent tracking of moving aerial targets [1,2,3]
We carried out several experiments with the aim to supply a complete analysis of the performance of the proposed SAR image target recognition method
We investigate different aspects: (i) we perform an experiment to evaluate the recognition accuracy of the proposed recognition method and (ii) we perform an in-depth comparative analysis of the performance of the proposed method with respect to the other recognition methods
Summary
As a new kind of reconnaissance remote sensing device, SAR is widely used in aerial and space reconnaissance, monitoring, and intelligent tracking of moving aerial targets [1,2,3]. UAVs are widely used in military surveillance, smart home monitoring, and target tracking. SAR image target recognition mainly refers to radar detection of targets, processing of echo information, and determination of target attributes, categories, or types. The inherent speckle noise will play a vital role in information extraction from SAR images. Being affected by the inherent speckle noise, SAR images are inferior in readability. The image features change tremendously as slight fluctuations of imaging parameters or the variation of surroundings which will affect the accuracy and speed of SAR image target recognition
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