Zongjie Cao

University of Electronic Science and Technology of China, Hua-yang, Sichuan, China

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Publications (29)15.81 Total impact

  • Zongjie Cao · Liyuan Xu · Jilan Feng
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    ABSTRACT: Automatic Target Recognition (ATR) performance of synthetic aperture radar (SAR) is highly dependent on the sensitivity of SAR images to observing angle. Hence, jointly using of multi-view images of the same target is an efficient way to improve ATR accuracy, since multi-view images carry more correlated information than single-view image. Taking into account heterogeneous multi-views with random not uniform observing interval, an ATR approach with joint sparse representation over a locally adaptive dictionary is investigated in this paper. The first step is to establish a locally adaptive dictionary using sparse representation (SR) after training samples dimension reduction process by Independent and Identically Distributed (IID) Gaussian random project matrix. The locally adaptive dictionary is able to alleviate the limitation of target pose by adjusting the use of information in images and between images with the interval changing. Then heterogeneous multi-view test samples are re-presented by selecting atoms from the locally adaptive dictionary using joint sparse representation (JSR). In such way, high recognition accuracy is guaranteed by combination of more target information and adjustment of the inter-correlation information guarantee. Experiments based on the Moving and Stationary Target Acquisition and Recognition database verify the performance of the proposed algorithm.
    No preview · Article · Jan 2016
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    Zongyong Cui · Zongjie Cao · Jianyu Yang · Hongliang Ren
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    ABSTRACT: A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants, L 1 -RNM, L 2 -RBM, and L 1 / 2 -RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.
    Preview · Article · Sep 2015 · Mathematical Problems in Engineering
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    ABSTRACT: This study proposes a novel non-negative matrix factorisation (NMF) variant L1/2-NMF after visualisation and analysis of the process of target recognition via NMF for synthetic aperture radar (SAR) images. NMF has been applied to obtain pattern feature in SAR images. This study considers the intrinsic character and the physical meaning of NMF feature when applied for SAR automatic target recognition. At the base of obtaining the linear relationship between the sample to be recognised and the train samples, the whole recognition process is detailed and vividly visualised. Meanwhile, lots of researches have been done to improve NMF methods by enforcing sparse constraint with L1-norm, such as non-negative sparse coding (NNSC), local NMF and sparse NMF. Compared with L1-norm, L1/2-norm has been shown to have a more natural sparseness. In this study, a novel variant of NMF with L1/2 constraint, called L1/2-NMF is proposed, and is carried out a thorough study by applying it in SAR target recognition. Experimental results on MSTAR public database show that both the basis and coding matrices obtained by L1/2-NMF have higher sparseness than those obtained by NMF, NNSC and NMF with sparseness constraints (NMFsc). The recognition results demonstrate that the L1/2-NMF outperforms NNSC, NMFsc and non-smooth NMF.
    No preview · Article · Jul 2015 · IET Radar Sonar ? Navigation
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    ABSTRACT: Cognitive robotic systems nowadays are intensively involving learning algorithms to achieve highly adaptive and intelligent behaviors, including actuation, sensing, perception and adaptive control. Deep learning has emerged as an effective approach in image-based robotic perception and actions. Towards cognitive robotic perception based on deep learning, this paper focuses the Constrained Restricted Boltzmann Machine (RBM) on visual images for sparse feature representation. Inspired by sparse coding, the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images. The RBM with Sparse Constraint (RBMSC) is proposed with a generalized optimization problem, where the constraints are applied on the probability density of hidden units directly to obtain more sparse representation. This paper presents three novel RBM variants, namely L 1-RBM, L 2-RBM, and L 1/2-RBM constrained by L 1-norm, L 2-norm, and L 1/2-norm on RBM, respectively. A Deep Belief Network with two hidden layers is built for comparison between each RBM variants. The experiments on MNIST database (Mixed National Institute of Standards and Technology database) show that the L 1/2-RBM can obtain more sparse representation than RBM, L 1-RBM, L 2-RBM, and Sparse-RBM (SRBM) in terms of sparseness metric. For further verification, the proposed methods are still tested on MNIST Variations dataset. The recognition results from perceived images in MNIST and MNIST Variations demonstrate that our proposed constrained RBM variants are feasible for object cognitive and perception, and the proposed L 1/2-RBM and L 1-RBM outperforms RBM and SRBM in terms of object recognition.
    Full-text · Article · Feb 2015 · Journal of Intelligent and Robotic Systems
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    Zongjie Cao · Yuchen Ge · Jilan Feng
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    ABSTRACT: Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated.
    Preview · Article · Nov 2014 · Journal on Advances in Signal Processing
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    Jilan Feng · Zongjie Cao · Yiming Pi
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    ABSTRACT: In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse representation-based classifiers (SRCs). We propose to take advantage of both polarimetric information and contextual information by combining sparsity-based classification methods with the concept of superpixels. Based on polarimetric feature vectors constructed by stacking a variety of polarimetric signatures and a superpixel map, two strategies are considered to perform polarimetric-contextual classification of PolSAR images. The first strategy starts by classifying the PolSAR image with pixel-wise SRC. Then, spatial regularization is imposed on the pixel-wise classification map by using majority voting within superpixels. In the second strategy, the PolSAR image is classified by taking superpixels as processing elements. The joint sparse representation-based classifier (JSRC) is employed to combine the polarimetric information contained in feature vectors and the contextual information provided by superpixels. Experimental results on real PolSAR datasets demonstrate the feasibility of the proposed approaches. It is proven that the classification performance is improved by using contextual information. A comparison with several other approaches also verifies the effectiveness of the proposed approach.
    Preview · Article · Jul 2014 · Remote Sensing
  • Jilan Feng · Zongjie Cao · Yiming Pi
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    ABSTRACT: This paper presents an SAR image classification approach that takes advantage of both amplitude and texture features. The proposed approach is based on superpixels obtained with some over-segmentation methods, and consists of two stages. In the first stage, the SAR image is classified with amplitude and texture feature used separately. Specifically, we use statistical model based maximum-likelihood method for amplitude based classification. Meanwhile, we classify the SAR image with the support vector machine (SVM) method by taking histograms generated with sparse coded morphological profiles as feature. To combine classification results produced with amplitude and texture features, a second refine stage is proposed based on the conditional random field (CRF) method. We define the CRF based on region adjacent graph (RAG) of superpixels. The unary term of the CRF is based on fusing classification scores produced by two classifiers in the first stage. Therefore, both of amplitude and texture information are used for the final classification. The graph cut (GC) algorithm is used to optimize the CRF model. We show experimental results on real SAR data, which verify the effectiveness of the proposed approach.
    No preview · Conference Paper · May 2014
  • Zongyong Cui · Zongjie Cao · Jianyu Yang · Jilan Feng
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    ABSTRACT: Synthetic aperture radar automatic target recognition (SAR ATR) has been widely applied in both military and civil fields. Much work has been done to improve the performance of SAR ATR systems in which feature extraction is an important step. To obtain pattern feature in SAR ATR, Non-negative Matrix Factorization (NMF), which is a dimensionality reduction method, has been applied by some researchers, although without deeper investigation. Meanwhile, in the computer vision field, lots of researches have been done to improve NMF methods by enforcing sparse constraint with L1-norm, like Non-negative Sparse Coding (NNSC), Local NMF (LNMF), and Sparse NMF (SNMF). Compared to L1-norm, L1/2-norm has been shown to have a more natural sparseness, however, little work has been done by using L1/2-norm constraint to NMF. In this letter, we propose a novel variant of NMF with L1/2 constraint, called L1/2-NMF, and carry out a thorough study by applying it in SAR target recognition. After mathematical derivation and analysis, the update rules of proposed L1/2-NMF are given in details. Experimental results on MSTAR public database show that both the basis and coding matrices obtained by L1/2-NMF have higher sparseness than those obtained by NMF, NNSC and NMF with Sparseness Constraints (NMFsc). The recognition results demonstrate that the proposed L1/2-NMF outperforms the other variants of NMF, like NNSC, NMFsc, and Nonsmooth NMF (NsNMF).
    No preview · Conference Paper · May 2014
  • Rui Min · Qianqian Yang · Yiming Pi · Zongjie Cao

    No preview · Article · Dec 2013 · Journal of Electronic Measurement and Instrument
  • Jifang Pei · Yulin Huang · Xian Liu · Jianyu Yang · Zongjie Cao · Bing Wang
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    ABSTRACT: Feature extraction from high-dimensional synthetic aperture radar (SAR) images is one of the crucial steps for SAR automatic target recognition (ATR). In this paper, we propose a new approach to SAR images feature extraction named Two-dimensional Principal Component Analysis-based Two-dimensional Maximum Interclass Distance Embedding (2DPCA-based 2DMIDE) which is based on manifold learning theory. The SAR image is projected into the feature space by horizontal 2DPCA and vertical 2DMIDE sequentially through this method. 2DPCA is efficient for image representation and preserves the global spatial structure of the original image, while 2DMIDE seeks to preserve the local spatial structure and the intrinsic geometry of the original image. Therefore, this feature extraction algorithm which fuses 2DPCA and 2DMIDE techniques can not only represent the original image in lower dimensions, but also excavate more powerful recognition information effectively. The experiment based on MSTAR database shows that the proposed method has a better recognition performance.
    No preview · Conference Paper · Nov 2013
  • Jilan Feng · Zongjie Cao · Yiming Pi
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    ABSTRACT: In this paper, we propose a variational multiphase segmentation framework for synthetic aperture radar (SAR) images based on the statistical model and active contour methods. The proposed method is inspired by the multiregion level set partition approaches but with two improvements. First, an energy functional which combines the region information and edge information is defined. The regional term is based on the G0 statistical model. The flexibility of G0 distribution makes the proposed approach to segment SAR images of various types. Second, we use fuzzy membership functions to represent the regions. The total variation of the membership functions is used to ensure the regularity. This not just guarantees the energy functional to be convex with respect to the membership functions but also enables us to adopt a fast iteration scheme to solve the minimization problem. The proposed method can segment SAR images of N regions with N - 1 membership functions. The flexibility of the proposed method is demonstrated by experiments on SAR images of different resolutions and scenes. The computational efficiency is also verified by comparing with the level-set-method-based SAR image segmentation approach.
    No preview · Article · Jul 2013 · IEEE Transactions on Geoscience and Remote Sensing
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    Zongjie Cao · Ying Tan · Jilan Feng
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    ABSTRACT: This paper presents a variational model based segmentation approach for polarimetric synthetic aperture radar (PolSAR) images. The formulation for PolSAR image segmentation is based on a scaled Wishart distribution based continuous Potts model, which can partition the image domain into distinct regions with respect to the statistical property of PolSAR data. To make the segmentation efficient, a duality based optimization approach is utilized to minimize the energy functional. Moreover, an automatic initialization approach which takes the unsupervised H–a classification result of the polarimetric data as input is used to initialize the segmentation process. This approach can estimate the appropriate number of clusters and the corresponding classification map for the PolSAR data, which are used as the input of the following variational segmentation approach. In such a way, the proposed approach is carried out in a fully unsupervised way. Both of the polarimetric decomposition features and the statistical characteristics are used to get the final segmentation result, which helps to increase the accuracy. Experimental results demonstrate the effectiveness of the proposed approach. Without any artificial supervision, the proposed approach can produce superior segmentation results than results obtained with random initialized variational approach and Wishart–H–a classification approach.
    Preview · Article · Feb 2013 · EURASIP Journal on Wireless Communications and Networking
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    Zongyong Cui · Zongjie Cao · Jianyu Yang · Jilan Feng
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    ABSTRACT: Synthetic aperture radar (SAR) automatic target recognition (ATR) is playing a very important role in military and civil field. Much work has been done to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance. Thus recognition using multiple classifiers fusion (MCF) has become a research hotspot in SAR ATR. Most current researchers focus on the fusion methods by parallel structure. However, such parallel structure has some disadvantages, such as large time consumption, features attribution conflict and low capability on confuser recognition. A hierarchical propelled strategy for multi-classifier fusion (HPSMCF) is proposed in this paper. The proposed HPSMCF has the characters both of series and parallel structure. Features can be used more effective and the recognition efficiency can be improved by extracting features and fusing the probabilistic outputs in a hierarchical propelled way. Meanwhile, the confuser recognition can be achieved by setting thresholds for the confidence in each level. Experiments on MSTAR public data demonstrate that the proposed HPSMCF is robust for variant recognition conditions. Compared with the parallel structure, HPSMCF has better performance both on time consumption and recognition rate.
    Preview · Article · Jan 2013 · EURASIP Journal on Wireless Communications and Networking
  • Zongjie Cao · Jilan Feng · Rui Min · Yiming Pi
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    ABSTRACT: Feature extraction is a very important step in Synthetic Aperture Radar automatic target recognition (SAR ATR). In this paper, a feature extraction procedure based on the nonnegative matrix factorization (NMF) and Fisher linear discriminant (FLD) analysis is proposed for target recognition in SAR images. Firstly, segmented SAR images are processed by the NMF algorithm, which can extract nonnegative features that contain the local spatial structure information of targets. Then the FLD method is applied to the extracted features, thus the discriminability of the features can be enhanced. Both the spatial locality and separability between classes are enforced by this two-phase feature extracting procedure. Finally, the obtained features are used for automatic target recognition. Compared to several other methods, experimental results show the effectiveness of the proposed method for target feature extraction and recognition in SAR images.
    No preview · Conference Paper · Jun 2012
  • Zongjie Cao · Lijia Chen
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    ABSTRACT: Because accurate identification cannot be obtained when the Identification Friend or Foe (IFF) sensor is employed separately, a radar sensor network (RSN) is designed to improve the identification capability in this paper. The content of this paper is focused on the information fusion algorithm, which is one of the key technologies in the RSN. The fuzzy c-means and the Bayesian network are chosen as the fusion algorithm. This algorithm can implement the identification friend or foe automatically after being trained by the training samples and expert's experience, and reduce the effect of uncertainties in the process of identification. At the same time, the algorithm can update the identification result with the augmentation of observations. The RSN can be expanded, if more information can be obtained, to adapt to the complicated environment, on the basis of this algorithm. The simulation results prove the validity and efficiency of the algorithm. Copyright © 2012 John Wiley & Sons, Ltd.
    No preview · Article · Jun 2012 · Security and Communication Networks
  • Zongjie Cao · Zongyong Cui · Yong Fan · Qi Zhang
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    ABSTRACT: A hierarchical feature fusion strategy based on Support Vector Machine (SVM) and Dempster-Shafer Evidence Theory is proposed for SAR image automatic target recognition in this paper. This strategy has three fusion hierarchies corresponding to three features. Principle Component Analysis (PCA), Local Discriminant Embedding (LDE) and Non-negative Matrix Factor (NMF) features are extracted from images without preprocessing, and are fed to SVM classifier. However, not all features are used in each fusion process. At each fusion process, an empirical threshold T is used to determine the used features and hierarchy depth. Experiments on MSTAR public data set demonstrate that the proposed strategy outperforms the system combining the outputs of three features directly.
    No preview · Conference Paper · Jan 2012
  • Lingzi Xue · Xiaqing Yang · Zongjie Cao
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    ABSTRACT: A building extraction method is proposed in this chapter used to deal with the special situation of SAR images processing. Morphological attribute profiles (APs) are the generalization of the recently proposed method morphological profiles (MPs). APs extract a multilevel characterization of an image by sequential application of morphological attribute filters. According to the type of the attributes considered in the morphological attribute transformation, different parametric features can be modeled. Here, the method proposed above is used for building extraction of SAR images. SVMs (Support vector machines) are used for classifications at last. The experimental analysis proved the usefulness of Aps in modeling the spatial information present in the image.
    No preview · Chapter · Jan 2012
  • Qianqian Yang · Rui Min · Zongjie Cao · Yiming Pi
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    ABSTRACT: The Compressive Sensing (CS) theory, which was first promulgated by Candes and Donoho in 2004, can break through the Nyquist Sampling Theorem to solve the bottleneck problems on practice such as the high-speed sampling and the mass-data storing. In this paper, the CS idea is applied to multi-baseline SAR tomography 3-D imaging. Aiming at solving the bad imaging quality problem caused by sparse baselines in practical applications, the sparse model in the height direction of multi-baseline SAR is built according to the limited scattering centers hypothesis. Thus the problem of imaging in the height direction is transformed to the problem of reconstructing a sparse signal. And the lp-norm minimization methods are utilized to solve this problem. Simulation results show that the proposed method not only can overcome the limitation of Rayleigh Criterion, but also can solve the problem brought about by the non-uniform distribution of baselines in noisy environment.
    No preview · Conference Paper · Jan 2012
  • Xin Gao · Zongjie Cao · Yingxi Zheng · Yong Fan · Qi Zhang
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    ABSTRACT: Feature extraction is the key technology and the core task of Synthetic Aperture Radar (SAR) target recognition. In this paper, a new target feature extracting method based on Sparse Non-negative Matrix Factorization (SNMF) is presented, which mainly use SNMF as the method to decompose the SAR target image and to construct the sparse feature vector. By this means, the similarity inside each cluster of the feature vectors is improved and the difference between the clusters is also raised. An identification test using the classification method of Support Vector Machine (SVM) demonstrates that the proposed method, compared to PCA, ICA and the general NMF feature extraction methods, can improve the stability and the accuracy of the target recognition significantly.
    No preview · Conference Paper · Jan 2012
  • Rui Min · Yating Hu · Yiming Pi · Zongjie Cao
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    ABSTRACT: SAR Tomography Imaging Using Sparse Bayesian Learning
    No preview · Article · Jan 2012 · IEICE Transactions on Communications