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Communication dans un congrès

Target Recognition in ISAR Images based on Relative Phases of Complex Wavelet Coefficients and Sparse Classification

Ayoub Karine 1 Abdelmalek Toumi 2 Ali Khenchaf 3 Mohammed Hassouni 4
2 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENSTAB_MOM_PIM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this paper, we present a novel approach for radar automatic target recognition on inverse synthetic aperture radar (ISAR). This approach is composed by two complementary steps: feature extraction and recognition. For the feature extraction step, we adopt a statistical modeling of the ISAR image in the complex wavelet domain. For doing so, we apply the dual-tree complex wavelet transform (DT-CWT) for each ISAR image in the database. After that, the relative phases of the resulting complex coefficients are computed. These relative phases are after statistically modeled using the Von Mises distribution. The estimated statistical parameters compose the feature vector of the ISAR images. Regarding to the recognition rate, the constructed feature vector is fed into the sparse representation based classification (SRC). More precisely, the training feature vectors are used as the atoms of a dictionary. The test feature vector is recognized according to its sparse linear combination with the dictionary. Extensive experiments and comparisons with other methods on ISAR images database demonstrate the effectiveness of the proposed approach.
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Communication dans un congrès
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Soumis le : jeudi 5 avril 2018 - 13:50:23
Dernière modification le : mercredi 5 août 2020 - 03:42:49

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  • HAL Id : hal-01759350, version 1

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Ayoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed Hassouni. Target Recognition in ISAR Images based on Relative Phases of Complex Wavelet Coefficients and Sparse Classification. 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP'2018), ATMS Advanced Technologies For Medicine and Signals, Mar 2018, Sousse, Tunisia. ⟨hal-01759350⟩

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