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For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios. [ABSTRACT FROM PUBLISHER]

In this letter, a novel classification via information aggregation through Dempster–Shafer's (DS) evidence theory has been presented to target recognition in a SAR image. Although the DS theory of evidence has been widely studied over the decades, less attention has been paid to its application for target recognition. To capture the characteristics of a SAR image, this letter exploits a new multidimensional analytic signal named monogenic signal. Since the components of the monogenic signal are of a high dimension, it is unrealistic to be directly used. To solve the problem, an intuitive idea is to derive a single feature by these components. However, this strategy usually results in some information loss. To boost the performance, this letter presents a classification framework via information aggregation. The monogenic components are individually fed into a recently developed algorithm, i.e., sparse representation-based classification, from which the residual with respect to each target class can be produced. Since the residual from a query sample reflects the distance to the manifold formed by the training samples of a certain class, it is reasonable to be used to define the probability mass. Then, the information provided by the monogenic signal can be aggregated via Dempster's rule; hence, the inference can be reached. [ABSTRACT FROM PUBLISHER]

This paper presents a conceptually simple, and robust, yet highly effective, approach to both texture classification and material categorization. The proposed system is composed of three components: 1) local, highly discriminative, and robust features based on sorted random projections (RPs), built on the universal and information-preserving properties of RPs; 2) an effective bag-of-words global model; and 3) a novel approach for combining multiple features in a support vector machine classifier. The proposed approach encompasses the simplicity, broad applicability, and efficiency of the three methods. We have tested the proposed approach on eight popular texture databases, including Flickr Materials Database, a highly challenging materials database. We compare our method with 13 recent state-of-the-art methods, and the experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for Columbia–Utrecht, 97.16% for Brodatz, 99.30% for University of Maryland Database, and 99.29% for Kungliga Tekniska högskolan-textures under varying illumination, pose, and scale. Moreover, the proposed approach significantly outperforms the current state-of-the-art approach in materials categorization, with an improvement to classification accuracy of 67%. [ABSTRACT FROM PUBLISHER]

In this letter, synthetic aperture radar (SAR) target recognition via classification on Riemannian geometry is presented. To characterize SAR images, which have broad spectral information yet spatial localization, a 2-D analytic signal, i.e., the monogenic signal, is used. Then, the monogenic components are combined by computing a covariance matrix whose entries are the correlation of the components. Since the covariance matrix, a symmetric positive definite one, lies on the Riemannian manifold, it is unreasonable to be dealt with by the standard learning techniques. To address the problem, two classification schemes are proposed. The first maps the covariance matrix into the vector space and feeds the resulting descriptor into a recently developed framework, i.e., sparse representation-based classification. The other embeds the Riemannian manifold into an implicit reproducing kernel Hilbert space, followed by least square fitting technique to recover the test. The inference is reached by evaluating which class of samples could reconstruct the test as accurately as possible. [ABSTRACT FROM PUBLISHER]

Signals with sinusoidal frequency modulation (FM) are the general form of micro-Doppler signals in radar echoes from targets with micro-motions. This paper derives the cyclic autocorrelation function of a sinusoidal FM signal using its cyclostationary characteristics, and estimates the parameters of amplitude, modulation index, micro-motion period and noise power based on cyclic autocorrelation function. The estimation method is robust in noise environment when compared with conventional time-frequency analysis methods. Finally, the performances of estimation by utilizing the simulation data and darkroom measurement are shown in tables. The Monte-Carlo simulation results show that the estimator is asymptotically optimal at SNR above −6 dB. [ABSTRACT FROM AUTHOR]