Browsing by Subject "SAR Images"

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    Man-made object classification in SAR images using 2-D cepstrum
    (IEEE, 2009-05) Eryildirim, A.; Çetin, A. Enis
    In this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE.
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    Off-grid sparse SAR image reconstruction by EMMP algorithm
    (IEEE, 2013) Uğur, Salih; Arıkan, Orhan; Gurbuz, A.C.
    A new and robust sparse SAR image reconstruction technique is proposed for off-grid targets in the CS framework. In the proposed approach, basis vectors corresponding to on-grid point reflectors are perturbed on a finer grid to find the appropriate bases for the reconstruction of off-grid targets. To provide efficiency of the reconstruction, the EMMP algorithm is applied to find reflectivity center locations. As demonstrated by simulations, the proposed approach significantly improves the performance of sparse SAR image reconstruction. © 2013 IEEE.
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    Sıkıştırılmış algılama kullanılarak nokta kipli SAR görüntü oluşturulması
    (2011-04) Uǧur, Salih; Arıkan, Orhan
    Bu çalışmada SAR gorüntü oluşturma yöntemi olarak sıkıştırılmış algılama tekniklerinden birisi olan LASSO kısıtlı en iyileştirme yaklaşımı kullanılmıştır. LASSO kapsamında kullanılan kısıt parametresi 𝜏 ’nun SAR görüntüsüne etkisini araştırmak amacı ile gerçek SAR görüntüleri üzerinde incelemeler yapılmıştır. Elde edilen görüntüler karşılaştırılmıştır. Seyreklik kısıt parametresi 𝜏 ’nun gürbüz seçimi için sinyal gürültü oranı ve ilintiye dayalı yeni bir metrik önerilmiştir. In this work, LASSO formulation, which is one of the comppessed sensing techniques, is used as a method of SAR image reconstruction. Simulations on the real SAR images are performed in order to analyze the effect of the τ parameter in LASSO formulation to the formed SAR imagery. Formed images are compared. A parameter, derived from signal to noise ratio and cross correlation, is suggested to robustly select the sparsity limit parameter τ. © 2011 IEEE.
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    Target detection and classification in SAR images using region covariance and co-difference
    (SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
    In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.
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    Target detection in SAR images using codifference and directional filters
    (SPIE, 2010) Duman, Kaan; Çetin, A. Enis
    Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search space. As a result the computational cost of the RC based algorithm significantly decreases. Images in MSTAR SAR database are first classified into several categories using directional filters (DFs). Target and clutter image features are extracted using RC and codifference methods in each class. The RC and codifference matrix features are compared using l 1 norm distance metric. Support vector machines which are trained using these matrices are also used in decision making. Simulation results are presented. © 2010 Copyright SPIE - The International Society for Optical Engineering.