- 标题
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- 实验方案
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An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture
摘要： Hyperspectral imaging requires handling a large amount of multidimensional spectral information. Hyperspectral image acquisition, processing, and storage are computationally and economically expensive and, in most cases, slow processes. In recent years, optical architectures have been developed for acquisition of spectral information in compressed form by using a small set of measurements coded by a spatial modulator. This article formulates a processing scheme that allows the measurements acquired by such compressive sampling systems to be used to perform spectral detection of targets, by adapting traditional detection algorithms for use in the compressive sampling model, and shows that the performance is comparable with that obtained by detection processes without compression.
关键词： hyperspectral imaging,compressive sensing,target detection,sparsity model
更新于2025-09-09 09:28:46
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Effective detection by fusing visible and infrared images of targets for Unmanned Surface Vehicles
摘要： The research progress for Unmanned Surface Vehicle (USV) is of great significance to human off-shore operations. Target detection is the foundation for USV applications. Ocean wave, frog, and illumination are the most important factors that affect exactness of target detection through visible and infrared images. This paper proposes an algorithm for weighted averaging fusion of visible/infrared images. Firstly, the visible light/infrared devices are required to collect the target surrounding information, perform feature analysis, and complete the anti-fog and de-noising preprocessing. These operations aim at improving the accuracy of image segmentation. Secondly, feature extractions of the visible and infrared target images are performed, respectively, and the recognition of the target image is further completed. Finally, image fusion is performed by weighted averaging of the targets detected by visible light and infrared images. The fusion uses a matching matrix to represent the similarity of the two images. When the two images are very similar, the image is fused by weighting pixels, which effectively improves the accuracy of the detection. Lots of simulations were conducted on MATLAB 2015a with a personal computer, and the results verified the success rate of target detection and recognition.
关键词： fusion,Multi-scale fractal,target detection,visible image,infrared image
更新于2025-09-09 09:28:46
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Ground moving target detection algorithm for monopulse-SAR based on complex monopulse ratio
摘要： Monopulse processing has been proved to be an effective way for airborne synthetic aperture radar (SAR)/ground moving target indication. In the monopulse-SAR system, moving target detection is applied to a complex ratio diagram, referred to as a complex monopulse ratio diagram (CMRD), generated through a pixel-by-pixel comparison between the range-Doppler (RD) spectra obtained from the difference and sum channels. This study examines the statistics of complex monopulse ratio (CMR) in the Gaussian background. Based on the statistics, a new random variable termed normalised CMR (NCMR) is derived by a linear transform to CMR. It is demonstrated that the conditional probability density function of NCMR, under the null hypothesis (H0), is independent of the clutter-plus-noise environment. Motivated by this characteristic, a constant false alarm rate detector using the modulus of NCMR as the test statistic is proposed to detect moving targets. The detection performance is evaluated with a closed-form expression of the probability of false alarm (PFA) derived. Moreover, the construction of the detector is extended to a so-called multi-CMRD form to accommodate even higher detection performance. Experimental results from two groups of real data are displayed to validate the statistical analysis and verify the detection performance.
关键词： monopulse-SAR,constant false alarm rate detector,complex monopulse ratio,Gaussian background,ground moving target detection
更新于2025-09-09 09:28:46
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Difference-based target detection using Mahalanobis distance and spectral angle
摘要： Two difference-based target detection methods are proposed in this work. In contrast to many target detectors which only calculate the distance between the testing pixel to the target spectrum, the proposed methods calculate the distance of the testing pixel to both of target and of background spectra. In other words, they utilize the difference between target and background computed distances. The first proposed method uses the Mahalanobis distance and benefits the valuable information contained in the statistics of targets and background. The second proposed method uses the kernel-based spectral angle mapper to benefit the advantages of spectral angle and kernel trick to separate targets from background, especially in non-linear cases. The experiments done on three real hyperspectral images indicate the high detection probability of the proposed methods compared to several target detectors.
关键词： hyperspectral imaging,Mahalanobis distance,target detection,spectral angle
更新于2025-09-09 09:28:46
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[IEEE 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) - Wuhan, China (2018.3.22-2018.3.23)] 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS) - Analysis of Target Detection Based on UWB NLOS Ranging Modeling
摘要： In recent years, indoor localization based on Ultra-Wide Band (UWB) system has been widely utilized due to its high precision and stability. However, Non-line-of-sight (NLOS) propagation is still one of the biggest problems for it can severely degrade the reliability of communication and localization accuracy. In this paper, we take advantage of NLOS and mainly focus on the target detection based on the ranging results of UWB device caused by NLOS. Therefore, some obstacle ranging experiments are designed and carried out by using the UWB equipment, such as a concrete obstacle moving between two ranging nodes, or passing through two ranging nodes, etc. And the ranging results due to the NLOS propagation are exhibited by deriving experimental results from real indoor environment. Then, the ranging results caused by NLOS are analyzed along with the obstacle moving. And the experimental results indicate that the moving obstacle can have a regular impact on NLOS ranging results for UWB system. Moreover, some strategies and methods are proposed to mitigate the NLOS error for UWB systems based on the experiments.
关键词： NLOS,UWB,Target detection,ranging modeling
更新于2025-09-09 09:28:46
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Extended Geometrical Perturbation Based Detectors for PolSAR Image Target Detection in Heterogeneously Patched Regions
摘要： Target detection in synthetic aperture radar image utilizing polarimetric information has attracted considerable attention. Single-target detector (STD), partial-target detector (PTD), and geometrical perturbation-polarimetric notch filter (GP-PNF) are three traditional polarimetric detectors based on polarimetric information. STD aims at detecting single targets, whereas PTD is suitable for partial targets. GP-PNF focuses on detecting targets with features, which are different from the homogeneous background. Both STD and PTD need a prior knowledge of the target, whereas GP-PNF needs to estimate the local clutter automatically. All these three methods use a feature vector to describe the character of the target or clutter. In fact, the feature vectors of the clutter and target may distribute in a subspace. Especially for the heterogeneous background, a feature vector cannot accurately describe the clutter. Motivated by this, this paper extends the clutter model from a complex feature vector to a complex feature subspace, which is suitable for a heterogeneously patched region and derives extended PTD and extended GP-PNF. Experimental results show the extended detectors’ validation and superiority to traditional detectors for target detection in heterogeneous regions.
关键词： Heterogeneous region,radar polarimetry,synthetic aperture radar (SAR),target detection
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - DIM Moving Target Detection using Spatio-Temporal Anomaly Detection for Hyperspectral Image Sequences
摘要： Dim moving target detection from hyperspectral image sequences, which contains temporal information as well as spectral information, has attracted researchers’ interest for its crucial role in civil and military application. In this paper, we propose a novel spatio-temporal anomaly approach to solve the dim moving target detection problem. This approach calculates spatial anomaly map, temporal anomaly map using anomaly detection algorithm from spatial domain and temporal domain, respectively. To achieve motion consistency characteristic, this approach manages to generate the trajectory prediction map. After fusing the spatial anomaly map, the temporal anomaly map and the trajectory prediction map, target of interest can be easily detected from background. The proposed approach is applied to a test dataset of airborne target in the cloud clutter background. Experimental results confirm that the proposed approach can achieve a low false alarm rate as well as a high probability of detection.
关键词： Hyperspectral imagery sequences,Spatial and temporal processing,Anomaly detection,Dim target detection
更新于2025-09-04 15:30:14
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Hyperspectral Anomaly Detection Using Collaborative Representation With Outlier Removal
摘要： Recently, collaborative representation detector (CRD) has been popularly used for hyperspectral anomaly detection. For the original CRD, the least squares solution becomes more unstable when more classes, i.e., samples for anomaly detection are involved, and the detection error is likely to happen if the test pixel is an anomalous pixel and several samples from background are similar anomalous. In this paper, we propose a hyperspectral anomaly detection method that uses CRD with principal component analysis (PCA) for removing outlier (PCAroCRD). According to the different background modeling methods, global and local versions are proposed. In the proposed algorithm, the spatial-domain PCA is adopted to extract main pixel information of global/local background that will be used as samples for collaborative representation, and simultaneously the information of abnormal pixels in global/local background can be removed. Fewer useful samples can also keep the detection result stable. Experimental results indicate that the PCAroCRD outperforms the original CRD, kernel version of CRD, advanced CRD (CRDBORAD), morphology-based CRD, Global Reed–Xiaoli (RX) algorithm, and the Local RX.
关键词： hyperspectral imagery,target detection,collaborative representation (CR),PCA,Anomaly detection
更新于2025-09-04 15:30:14
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou, China (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hyperspectral Target Detection Based on a Spatially Regularized Sparse Representation
摘要： Sparse representation (SR) is an effective method for target detection in hyperspectral imagery (HSI). The structured dictionary is arranged according to the target class and the background class, the sparse coefficients associated with each dictionary element of a given test sample can be recovered by solving an (cid:2)1-norm minimization problem. It is possible to introduce further regularization to improve the detection performance. The classical SR detection algorithms does not consider the spatial information of the detected pixels. It can be expected that sparse coefficients of adjacent pixels are similar due to the spatial correlation. This paper proposes a novel SR model which takes into account a spatial regularization term to promote the piecewise continuity of the sparse vectors. The formulated problem is solved via alternating direction method of multipliers (ADMM). We illustrate the enhanced performance of the proposed algorithm via both synthetic and real hyperspectral data.
关键词： spatial correlation constraint,sparse representation,ADMM,target detection,Hyperspectral imagery
更新于2025-09-04 15:30:14
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Infrared Patch-tensor Model with Weighted Tensor Nuclear Norm for Small Target Detection in A Single Frame
摘要： The robust and ef?cient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA) based method have been developed recently, which have achieved state-of-the-art performance. However, there are still two drawbacks: 1) the false alarm ratio would raise under the heavy background clutters and noises, 2) these methods are usually time-consuming and not suitable for real-time processing. To solve this problem, an infrared patch-tensor model based on weighted tensor nuclear norm is proposed in this paper. First, the infrared image is transformed into the infrared patch-tensor (IPT). Considering the sum of nuclear norms (SNN) adopted in the IPT model is not the convex envelope of the tensor rank, and the solution is substantially suboptimal. The tensor nuclear norm is adopted to recover the underlying low-rank background tensor and spare target tensor, and the computation complexity can be reduced dramatically with the help of the tensor Singular Value Decomposition (t-SVD). Moreover, to further suppress the background clutters, a weight tensor is incorporated with tensor nuclear norm to preserve the background edges better. Then the separation between target and background is formulated as a convex weighted tensor RPCA (TRPCA) model. Finally, the proposed model can be solved by the Alternating Direction Method of Multipliers (ADMM). Extensive experiments demonstrate that the proposed model outperforms the other state-of-the-arts in performance and ef?ciency.
关键词： Infrared patch-tensor model,small target detection,weighted tensor robust principal component analysis
更新于2025-09-04 15:30:14