Lingjun Zhao

Extended local binary patterns for texture classification

This paper presents a novel approach for texture classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed approach, two different and complementary types of features (pixel intensities and differences) are extracted from local patches. The intensity-based features consider the intensity of the central pixel (CI) and those of its neighbors (NI); while for the difference-based feature, two components are computed: the radial-difference (RD) and the angular-difference (AD). Inspired by the LBP approach, two intensity-based descriptors CI-LBP and NI-LBP, and two difference-based descriptors RD-LBP and AD-LBP are developed. All four descriptors are in the same form as conventional LBP codes, so they can be readily combined to form joint histograms to represent textured images. The proposed approach is computationally very simple: it is totally training-free, there is no need to learn a texton dictionary, and no tuning of parameters. We have conducted extensive experiments on three challenging texture databases (Outex, CUReT and KTHTIPS2b). Outex results show significant improvements over the classical LBP approach, which clearly demonstrates the great power of the joint distributions of these proposed descriptors for gray-scale and rotation invariant texture classification. The proposed method produces the best classification results on KTHTIPS2b, and results comparable to the state-of-the-art on CUReT.

An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The G 0 distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.

SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification

Since superpixel takes spatial relationship between pixels into account, which makes the image classification process more understandable and the results more satisfactory, superpixel-based classification methods have been widely studied in recent years. However, due to speckle noise, traditional superpixel generating algorithms still have some drawbacks for synthetic aperture radar (SAR) image. In this letter, we propose a novel superpixel generating algorithm based on pixel intensity and location similarity (PILS) for SAR image. In addition, for the sake of image classification, features of Gabor filters and gray level co-occurrence matrix (GLCM) are extracted from each superpixel. The proposed superpixel generating method has the following three characteristics: (1) the terrain boundaries of SAR image are preserved well; (2) the method has more robustness against speckle noise; and (3) it has high computational efficiency. Experiments on synthetic and real SAR images demonstrate that our method significantly outperforms several state-of-the-art superpixel methods and PILS superpixel-based classification obtains better results than other pixel-based methods.

Polarimetric Scattering Similarity Between a Random Scatterer and a Canonical Scatterer

In this letter, we propose a novel parameter to measure the scattering similarity between a random scatterer and a canonical scatterer. Compared with the similarity parameter proposed by Yang, the novel parameter not only has some advantages, such as its independence of the spans of a coherence matrix, but also can be applied directly in the case of a random scatterer made up of multiscattering centers. As an example, the novel parameter is adopted to extract some scattering characteristics of a target. With the full polarimetric L-band airborne synthetic aperture radar data, we illustrate the veracity of the novel parameter in measuring scattering similarity and its application in terrain classification.

Automated flood detection with improved robustness and efficiency using multi-temporal SAR data

Flood detection from synthetic aperture radar (SAR) images should be performed with accurate, stable, automated and time-efficient algorithms; however, few methods meet all these requirements. Recently, Giustarini et al. proposed an automated promising methodology, capable of providing satisfactory results in flood detection. The algorithm is based on the assumption that a flood image contains a relatively high number of pixels with low backscatter values, exhibiting a bimodal histogram. For the case of a histogram that is not bimodal, the optimization of the theoretical curve describing the water pixels has to be manually constrained in a user-defined range. To overcome this shortcoming, this letter proposes an alternative procedure for core water body identification. First, by thresholding the difference image, derived by change detection between the flood and reference images, a mask of core water bodies is identified. Then, the mask is applied on the flood image, to extract the water pixels located in the core water bodies and straightforwardly derive the statistical curve describing water pixels. Successively, a sequence of thresholding, region growing and change detection is applied. The experimental results with two pairs of SAR images show that the proposed automated algorithm is stable and time-efficient, and provides accurate results.

Polarimetric SAR Target Detection Using the Reflection Symmetry

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.

Contour matching using the affine-invariant support point set

Moment has been widely used for contour matching. To use the moment to achieve contour matching under affine transformations, the affine-invariant support point set (SPS) should be constructed first. Then, a novel method of acquiring SPS based on the contour projection (SPS-CP) is proposed here. For an arbitrary selected contour point, the contour is projected onto the line vertical to the vector connecting the contour centroid and the selected point, and the contour points with the sampled projection values are picked up to form the SPS-CP of the point. SPS-CP which captures the global structure of the contour is stably affine-invariant. Experiments on synthetic and real data demonstrate that moments generated from SPS-CP outperform those generated from SPSs sampled by uniform spacing or affine length.

An Optimization Procedure of the Lagrange Multiplier Method for Polarimetric Power Optimization

The Lagrange multiplier method is one of the basic optimization procedures to find the optimum polarizations for the incoherent scattering case. This letter proves for the first time that a fixed relationship exists between the optimum polarization and the Lagrange multiplier. Then, an optimization procedure is proposed to simplify the computational complexity of the Lagrange multiplier method. To speed up the convergence of the proposed procedure, the minimum search intervals are discussed and given theoretically. A numerical example is shown to demonstrate the effectiveness of the proposed procedure.

Joint sparse representation of monogenic components: With application to automatic target recognition in SAR imagery

In this paper, classification via joint sparse representation of the monogenic signal is presented for target recognition in SAR imagery. First, the monogenic signal is performed to capture the characteristics of SAR image. Since it is infeasible to directly apply the raw component to classification due to the high data dimension and redundancy, three augmented feature vectors are defined via uniform downampling of the real part, the imagery part, and the instantaneous phase. The monogenic features are then fed into a recently developed framework, sparse representation-based classification (SRC). Rather than produce individual sparse pattern, this paper generates the similar sparsity pattern for three feature vectors by imposing a mixed norm on the representation matrix. Extensive experiments on MSTAR database demonstrate that the proposed method could significantly improve the recognition accuracy.