Automatic target recognition has been widely studied over the years, yet it is still an open problem. The main obstacle consists in extended operating conditions, e.g.., depression angle change, configuration variation, articulation, and occlusion. To deal with them, this paper proposes a new classification strategy. We develop a new representation model via the steerable wavelet frames. The proposed representation model is entirely viewed as an element on Grassmann manifolds. To achieve target classification, we embed Grassmann manifolds into an implicit reproducing Kernel Hilbert space (RKHS), where the kernel sparse learning can be applied. Specifically, the mappings of training sample in RKHS are concatenated to form an overcomplete dictionary. It is then used to encode the counterpart of query as a linear combination of its atoms. By designed Grassmann kernel function, it is capable to obtain the sparse representation, from which the inference can be reached. The novelty of this paper comes from: 1) the development of representation model by the set of directional components of Riesz transform; 2) the quantitative measure of similarity for proposed representation model by Grassmann metric; and 3) the generation of global kernel function by Grassmann kernel. Extensive comparative studies are performed to demonstrate the advantage of proposed strategy.

In this letter, the classification via sparse representation of the monogenic signal is presented for target recognition in SAR images. To characterize SAR images, which have broad spectral information yet spatial localization, the monogenic signal is performed. Then an augmented monogenic feature vector is generated via uniform down-sampling, normalization and concatenation of the monogenic components. The resulting feature vector is fed into a recently developed framework, i.e., sparse representation based classification (SRC). Specifically, the feature vectors of the training samples are utilized as the basis vectors to code the feature vector of the test sample as a sparse linear combination of them. The representation is obtained via l1-norm minimization, and the inference is reached according to the characteristics of the representation on reconstruction. Extensive experiments on MSTAR database demonstrate that the proposed method is robust towards noise corruption, as well as configuration and depression variations.

This paper addresses the statistical segmentation of SAR (Synthetic Aperture Radar) image combining PM (Perona Malik) nonlinear diffusion model and MRF (Markov Random Field) model. First, the original SAR image is filtered using the modified PM nonlinear diffusion model, in which the diffusion coefficients along the tangent direction and the normal direction are approximated and simplified. Afterwards, the filtered image is segmented using MRF model, in which the clique potential is computed using both the label configuration and the intensity information. The proposed method is marked by PM-MRF for short. Experimental results show that PM-MRF competes favorably with the traditional one to segment SAR image homogeneously.

This paper addresses the Polarimetric synthetic aperture radar (PolSAR) CFAR target detection utilizing the PolSAR synthesis technique. The optimal polarization ellipticity and orientation angles, which maximizes the ratio of the antenna receiver power between target and clutter (SCR), are searched in the co-polarized and cross-polarized channels, and the obtained antenna receiver power is named as the polarization synthesis enhancement (PSE) metric. Using the PSE metric, a data fitting based target detection scheme is presented. First, the Fisher distribution, which has extensive modeling capacity in a large set of clutters, is utilized to fit the distribution of PSE metric. Then, the corresponding “Second Kind Statistics” (SKS) parameter estimator is presented and the numerical solution of the detection threshold is also derived. Afterward, the CFAR detection is implemented. The experimental results demonstrate the enhancement results of PSE metric are almost comparable with that of the Polarimetric Matched Filter (PMF) detector. However, the computation load of PSE metric is generally less than that of the PMF. Moreover, the target detection results show the CFAR detector based on the Fisher distribution can realize the accurate target detection in homogenous and heterogeneous clutter areas.

This letter addresses the polarimetric synthetic aperture radar target detection using the magnitude of the (2, 3) term in the sample averaged coherency matrix. The theoretical analysis demonstrates that such term reveals the difference between the nonreflection symmetric targets and natural clutters. The statistical models for such term are derived within different degrees of homogeneity. Based on the statistical models, an automatic constant-false-alarm-rate detection scheme is completed. The parameter estimation and the solution for the detection threshold are given in detail. Experimental results demonstrate the capability of the proposed approach for detecting ships, oil stores, buildings, etc., in homogeneous and heterogeneous areas.