Zongjie Cao

Optimized projection matrix for compressive sensing

Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.

Fast target detection method for high-resolution SAR images based on variance weighted information entropy

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.

UWB LFM echo signal detection and time-delay estimation based on compressive sensing

UWB linear frequency modulated (LFM) signals are widely used in radar, sonar and communication systems. In some applications, the detection of LFM signals and estimation of time-delay are very important. It needs very high sampling rate to address the problems for UWB LFM signal under Nyquist sampling theory which exceeds the current ADC capacity. In this paper, we propose a Compressive Sensing (CS) based method to solve the problem with ultra low sampling rate. We adopt an FrFt based sparse dictionary for CS because of the energy concentration property of LFM signal in the fractional Fourier domain. The performance is much better than the already existed method which used signal-matched sparse dictionary in noise condition. Experiments based on simulated data are carried out to testify the results.

Information Theory-Based Target Detection for High-Resolution SAR Image

In this letter, we propose a target detection approach in high-resolution synthetic aperture radar (SAR) images by using the information measurement of superpixels. This study aims to transform the basic cell of SAR images from pixel to patch through the superpixel algorithm. Moreover, by taking advantage of the rich statistical character of the patch, an information measurement, including self-information and entropy, is utilized to measure the statistical difference between patches. Self-information is utilized to measure the relative information value of patches, while entropy is used to describe the change degree of statistical characteristics between the patch and its surroundings. In this way, the proposed approach is more stable for SAR images with different intensities of speckle noise because damaging information measurement is difficult with speckle noise. Information on distributed targets in high-resolution SAR images can be measured via superpixel-based pixel clustering instead of by single pixels. Therefore, the proposed method can utilize more information to achieve target detection. The experimental data contain simulated SAR images with different intensities of speckled noise and real high-resolution SAR images. The performance of the proposed approach is validated by comparing the proposed approach with two classic constant-false-alarm-rate algorithms on the experimental data.

Target detection in complex scene of SAR image based on existence probability

This study proposes a target detection approach based on the target existence probability in complex scenes of a synthetic aperture radar image. Superpixels are the basic unit throughout the approach and are labelled into each classified scene by a texture feature. The original and predicted saliency depth values for each scene are derived through self-information of all the labelled superpixels in each scene. Thereafter, the target existence probability is estimated based on the comparison of two saliency depth values. Lastly, an improved visual attention algorithm, in which the scenes of the saliency map are endowed with different weights related to the existence probabilities, derives the target detection result. This algorithm enhances the attention for the scene that contains the target. Hence, the proposed approach is self-adapting for complex scenes and the algorithm is substantially suitable for different detection missions as well (e.g. vehicle, ship or aircraft detection in the related scenes of road, harbour or airport, respectively). Experimental results on various data show the effectiveness of the proposed method.

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

In this paper a novel incremental nonnegative matrix factorization (INMF) for SAR target recognition is proposed in order to overcome the defect that conventional methods have in online processing. When training samples increase, unlike conventional NMF-based methods computing both original and new samples to retrain a new model, INMF just computes the new samples to update the trained model incrementally, which can avoid repetitive learning original samples and reduce the computation cost. Meanwhile, the INMF with Lp constraint is proposed by setting the updating process under Lp sparse constraint for matrices decomposition. The proposed Lp-INMF can solve the problem of the computational cost increasing along with the sample increasing which is common in traditional methods. Experiment results on MSTAR data verify that the recognition performance obtained by Lp-INMF outperforms other traditional methods, and the recognition efficiency can be improved.

SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks

In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the traditional procedure: detection, discrimination, classification and recognition. Each step is supposed to provide optimal processing results for the next step, but this is difficult to implement in real-life applications because of speckle noise and inefficient connection among these procedures. To solve this problem, the RCNN is applied to large scene SAR target recognition, which can detect the objects while recognizing their classes based on its regression method and the sharing network structure. However, size of the input images to RCNN is limited so that the classification could be accomplished, which leads to a problem that RCNN is not able to handle the large scene SAR images directly. Thus, before the RCNN, a fast sliding method is proposed to segment the scene image into sub-images with suitable size and avoid dividing targets into different sub-images. After the RCNN, candidate regions on different slices are predicted. To locate targets on large scene SAR images from these candidate regions on small slices, the Non-maximum Suppression between Regions (NMSR) is proposed, which could find the most proper candidate region among all the overlapped regions. Experiments on 1476 × 1784 simulated MSTAR images of simple scenes and complex scenes show that the proposed method can recognize all targets with the best accuracy and fastest speed, and outperform the other methods, such as constant false alarm rate (CFAR) detector + support vector machine (SVM), Visual Attention+SVM, and Sliding-RCNN.

Target Detection in High-Resolution SAR Images via Searching for Part Models

In high-resolution synthetic aperture radar (SAR) images, targets are often spatially spread, and some separated regions of one target may be detected as different potential targets. The target should be represented by a part model to combine the separated regions into one target. In this letter, a part often means a separated region, and the part model obtains a hierarchical combination of the other parts. Moreover, the bottom hierarchy consists of strong scatters in the local SAR image, which are searched based on compressed intensities. The middle hierarchy consists of parts, which are generated based on connectivity and similarity of scatters in the bottom hierarchy. The top hierarchy delineates the relationship of parts in the middle hierarchy. A search method is designed based on the different features of the elements in each hierarchy. Consequently, the proposed algorithm combines different traits of targets to improve detection, and unites the separated regions of one target in the top hierarchy. This algorithm is validated by SAR images with different resolutions and scenes.

A Two-Stage Target Detection Method for High-Resolution SAR Images

Since that traditional CFAR is not suitable for high resolution target detection of SAR images, in this paper, a new two-stage target detection method is proposed. On the first stage, we extract ROIs from the SAR image based on the variance weighted information entropy (WIE). The rough ROIs are further processed with a series of methods, including false alarm exclusion, rectangular-completing and centroid alignment. On the second stage, for each ROI, we adopt a variational segmentation algorithm to accurately extract the target. In our experiment, in particular, we test the proposed method on a real SAR image, and its effectiveness is successfully demonstrated.