- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Low-rank and sparse matrix decomposition with background position estimation for hyperspectral anomaly detection
摘要： Hyperspectral anomaly detection (AD) has attracted much attention over the last 20 years. It distinguishes pixels with significant spectral differences from the background without any prior knowledge. The low-rank and sparse matrix decomposition (LRaSMD)-based detector has been applied to AD, where the anomaly value is measured by Euclidean distance based on the sparse component. However, the background interference in sparse component seriously increases the false alarm rate and influences the detection of real anomalies. In this paper, a novel AD method based on LRaSMD and background position estimation is proposed, which aims to suppress background interference in the sparse component for a better separation between background and anomalies. Firstly, the original sparse matrix is obtained using the traditional LRaSMD method. Secondly, the abundance maps are constructed by the sequential maximum angel convex cone (SMACC) endmember extraction model. Thirdly, considering that the anomalies occupy only a few pixels with a low probability, the coordinate positions of background pixels are estimated through these abundance maps. Finally, the spectra corresponding to these positions in the original sparse matrix are replaced with zero vectors, and the final anomaly value is calculated based on the improved sparse matrix. The proposed method achieves an outstanding performance by considering both the spectral and spatial characteristics of anomalies. Experimental results on synthetic and real-world hyperspectral datasets demonstrate the superiority of the proposed method compared with several state-of-the-art AD detectors.
关键词： Anomaly detection,Background estimation,Low-rank and sparse matrix decomposition,Hyperspectral imagery,Endmember extraction
更新于2025-09-23 15:23:52
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A stacked autoencoders-based adaptive subspace model for hyperspectral anomaly detection
摘要： In recent years, some adaptive subspace models perform well for hyperspectral anomaly detection (AD). In this paper, a stacked autoencoders-based adaptive subspace model (SAEASM) is proposed. First, three windows, namely, inner, outer and dictionary window, centered at the test point are used to obtain the local background pixel points and dictionary in the hyperspectral image (HSI). Second, the deep features of differences between the test point and the local dictionary pixels are first acquired by the use of SAE architectures. Then, the deep features of differences between the local background pixels and the local dictionary pixels are also acquired by the use of SAE architectures. Finally, the detection result is obtained by the stacked autoencoders-based adaptive subspace model that is based on the 2-norm of the above two deep features. The experimental results carried out on real and synthetic HSI demonstrate that the proposed SAEASM generally performs better than the comparison algorithms.
关键词： Hyperspectral image,Stacked autoencoders,Adaptive subspace,Anomaly detection
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection
摘要： This paper presents a novel method for hyperspectral anomaly detection considering the spectral redundancy and exploiting spectral-spatial information at the same time. We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize spectral-spatial information, the cubic data (two spatial dimensions and one spectral dimension) is treated as a 3-order tensor. Then the non-local self-similarity is fully explored in our method. For the reason to reduce the ringing artifacts caused by over-lapped segmentation in exploring the non-local self-similarity, we introduce the hyper-Laplacian constrained low-rank tensor decomposition and we get the separated background and residual parts. Finally, to eliminate the effect of Gaussian noise, we use local-RX basic detector to detect the residual matrix. Experimental results on two real hyperspectral data sets verified the effectiveness of the proposed algorithms for HSI anomaly detection.
关键词： low-rank tensor decomposition,hyperspectral anomaly detection,Dimensionality reduction
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Anomaly Detection Using Compressed Columnwise Robust Principal Component Analysis
摘要： This paper proposes a compressed columnwise robust principal component analysis (CCRPCA) method for hyperspectral anomaly detection. The CCRPCA improves the regular RPCA by using the Hadamard random projection and constraining the columnwise structure of sparse anomaly matrix. The Hadamard random projection reduces the computational cost of the hyperspectral data, and the columnwise sparse structure alleviates negative effects from the anomalies on the columns of the background. The sparse anomaly matrix and the background matrix are estimated by optimizing a convex program, and the anomalies are estimated from nonzero columns of the compressed sparse matrix. Preliminary experiment result from the San Diego dataset shows that the CCRPCA outperforms four state-of-the-art detection methods in both the receiver operating characteristic curve and the area under curve.
关键词： anomaly detection,Hyperspectral imagery,columnwise robust principal component analysis,Hadamard random projection
更新于2025-09-23 15:23:52
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Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery
摘要： Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly detection is the RX detector. A number of variations have been studied since then, including global and local versions for different type of anomalies. Aiming at a real-time requirement for practical applications, this paper extends the concept of global and local anomaly detectors to be real-time detectors. The algorithms exploit the fact that a true real-time detector must produce its output in a causal manner and at the same time as an input comes in. Taking advantage of the Woodbury matrix identity, the global and local real-time detectors can be implemented and processed pixel-by-pixel in real time. Both synthetic and real hyperspectral imagery are conducted to demonstrate their performance.
关键词： sliding local window,Woodbury matrix identity,hyperspectral remote sensing,anomaly detection,real-time
更新于2025-09-23 15:22:29
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Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery
摘要： Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coef?cient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.
关键词： hyperspectral image (HSI) analysis,RX,hierarchical structure,anomaly detection
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Performance and Techno-Economic Evaluation of a Three-Phase, 50-kW SiC-Based PV Inverter
摘要： We address the problem of detecting a stealth aircraft flying far away from an observer with limited visibility conditions using their multispectral signature. In such environment, the aircraft is a very low-contrast target, i.e., the target spectral signature may have a similar magnitude to the background clutter. Therefore, methods accounting only for the spectral features of the target, while leaving aside its spatial pattern, may either lead to poor detection statistics or high false alarm rate. We propose a new detection method which accounts for both spectral and spatial dispersions, by inferring level sets of the Mahalanobis transform of the multispectral image. This combines the approach of the well-known Reed Xiaoli (RX) detector with some elements of the level set methods for shapes analysis. This algorithm is in turn used to specify the wavelength bands which maximize an aircraft detection probability, for a given false alarm rate. This methodology is illustrated in a typical scenario, consisting of a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, over a database of 30 000 simulated multispectral infrared signature (IRS). The results emphasize that, in the context of aircraft detection, there is great interest in using multispectral IRS rather than integrated IRS, as long as the IR bands are well chosen.
关键词： Aircraft detection,multispectral infrared signature (IRS),spectral band selection,anomaly detection
更新于2025-09-23 15:19:57
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Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen
摘要： The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a ‘clean’ library. And, a high coefficient of determination (R2 LAD = 0.9984 and R2 PLS-DA = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control.
关键词： Soybean meal,Untargeted detection,Near-infrared hyperspectral/microscopic imaging,Local anomaly detection,Near-infrared spectroscopy
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - The Microwave Oven Thermal Field Uniformity Increasing by Using Powermeter
摘要： We address the problem of detecting a stealth aircraft flying far away from an observer with limited visibility conditions using their multispectral signature. In such environment, the aircraft is a very low-contrast target, i.e., the target spectral signature may have a similar magnitude to the background clutter. Therefore, methods accounting only for the spectral features of the target, while leaving aside its spatial pattern, may either lead to poor detection statistics or high false alarm rate. We propose a new detection method which accounts for both spectral and spatial dispersions, by inferring level sets of the Mahalanobis transform of the multispectral image. This combines the approach of the well-known Reed Xiaoli (RX) detector with some elements of the level set methods for shapes analysis. This algorithm is in turn used to specify the wavelength bands which maximize an aircraft detection probability, for a given false alarm rate. This methodology is illustrated in a typical scenario, consisting of a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, over a database of 30 000 simulated multispectral infrared signature (IRS). The results emphasize that, in the context of aircraft detection, there is great interest in using multispectral IRS rather than integrated IRS, as long as the IR bands are well chosen.
关键词： Aircraft detection,multispectral infrared signature (IRS),spectral band selection,anomaly detection
更新于2025-09-19 17:13:59
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[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - Correlations between generation output of two different photovoltaic power stationsa??a??based on the field data of Xinjiang
摘要： This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies, and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.
关键词： unsupervised spectral unmixing,Hyperspectral imagery,MCMC,Bayesian estimation,anomaly detection
更新于2025-09-19 17:13:59