Aircraft Target Recognition using Copula Joint Statistical Model and Sparse Representation Based Classification
Résumé
This paper proposes a new target recognition method for inverse synthetic aperture radar (ISAR) images. This method is based on joint statistical modeling of the complex wavelet coefficients for ISAR image characterization and the sparse representation based classification (SRC) for the recognition. To extract features from an ISAR image, we first transform it in the complex wavelet domain using the dual-tree complex wavelet transform (DT-CWT). Then, we compute magnitude information for each complex subband. After that, we propose a joint statistical model for magnitude distribution, that takes into account the dependences between different orientations and scales. To do so, we adopt the copula as a multivariate model thanks to its suitability to capture jointly the subband marginal distribution and the dependence structure. For the recognition step, we exploit SRC which recovers the test descriptor to classify over a given dictionary composed by the training descriptors. This method classifies the test sample as the class whose training samples can generate the minimum sparse representation error. Experimental results on ISAR images database show that using copula and sparse classifier improve significantly the recognition rates compared to classical models and classifiers.