- 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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Classification of Urban Hyperspectral Remote Sensing Imagery Based on Optimized Spectral Angle Mapping
摘要： Hyperspectral remote sensing imagery provides highly precise spectral information. Thus, it is suitable for the land use classification of urban areas that are composed of complicated structures. In this study, a new spectral angle and vector mapping (SAVM) classification method, which adds a factor based on ''the differences in the spectral vector lengths'' among image pixels to the spectral angle mapping (SAM) classification method, is proposed. The SAM and SAVM methods were applied to classify the aerial hyperspectral digital imagery collection experiment imagery acquired from the business district of Washington, DC, USA. The results demonstrated that the overall classification accuracy of the SAM was 64.29%, with a Kappa coefficient of 0.57, while the overall classification accuracy of the SAVM was 81.06%, with a Kappa coefficient of 0.76. The overall classification accuracy was improved by 16.77% by the SAVM, indicating that the use of a SAVM classification method that considers both the spectral angle between the reference spectrum and the test spectrum and the differences in the spectral vector lengths among image pixels can improve the classification accuracy of urban area with hyperspectral remote sensing imagery.
关键词： Hyperspectral imagery,Spectral angle and vector mapping (SAVM),Classification,Spectral angle mapping (SAM)
更新于2025-09-23 15:23:52
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Distinguishing between closely related species of Allium and of Brassicaceae by narrowband hyperspectral imagery
摘要： Classification of crop species is an actively studied topic in remote sensing using multi-spectral image sensors. Unfortunately, the spectral bands available in the multispectral imagery are broad and limited in number to classify the crop species. In this paper, we propose optimal spectral bands to classify Allium (garlic and onion) and Brassicaceae (Chinese cabbage and radish) by using higher-dimensional data from hyperspectral imagery. A decision-tree classifier was used to determine the optimal method to use the high-dimensional data. The high-dimensional data were analysed for all growth stages and considering bandwidths with different full width at half maximum (FWHM) values at 25, 40, 50 and 80 nm. The spectral bands selected for Allium were differentiated into green, blue, and NIR bands for each growth stage. The results show that Allium can be classified clearly as overall accuracy (OA) 1 and kappa coefficient 1 for all FWHM based on March 22 data. For each April 19 and May 12 data, the decision-tree classifier with each 80 nm FWHM and 50 nm FWHM yielded a better classification accuracy of more than OA 0.921 and kappa coefficient 0.839 than other FWHM. The spectral bands selected for Brassicaceae were found to be similar to blue band for all growth stages. Brassicaceae was classified clearly for all FWHM based on October 27 data. Also, Brassicaceae was classified clearly for 25 nm FWHM based on November 25 data and OA, kappa coefficient for 40 nm FWHM and 50 nm FWHM are high as 0.974, 0.947 respectively.
关键词： Decision-tree classifier,Hyperspectral imagery,Classification,Full width at half maximum,Spectral band
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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Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction
摘要： In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time.
关键词： hyperspectral imagery,spectral unmixing,Constraints,nonnegative tensor factorization
更新于2025-09-23 15:22:29
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Analysis for the Weakly Pareto Optimum in Multiobjective-Based Hyperspectral Band Selection
摘要： Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. A possible drawback of these methods is that they can only generate one solution in a single run with a given band number. To overcome this problem, multiobjective (MO)-based methods, which were able to simultaneously obtain a series of subsets with different band numbers, were investigated for band selection. However, because the range of band selection problem is discrete, recently proposed weighted Tchebycheff (WT)-based MO methods may suffer weakly Pareto optimal problem. In this case, the solutions for each band number will be nonunique and no optimal solution exists. Decision makers have to manually select a unique solution for each band number. In this paper, we provide a theoretical analysis about the weakly Pareto optimal problem in band selection, and quantitatively give the boundary conditions. Moreover, we further summarize the suggestions which will help users avoid the weakly Pareto optimal problem. According to these criteria, we develop a new adaptive-penalty-based boundary intersection (APBI) framework to improve the MO algorithm in hyperspectral band selection. APBI mainly includes two advantages: 1) avoiding weakly Pareto optimum and 2) reducing the sensibility of the penalty factor. The theoretical analysis is further validated by contrast experiments. The results demonstrate that the weakly Pareto optimal solutions really exist in WT methods, while APBI can overcome this problem.
关键词： multiobjective (MO) optimization,Band selection,weakly Pareto optimum,hyperspectral imagery (HSI)
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hyperspectral Image Classification VIA a Joint Sparsity and Spatial Correlation Model
摘要： In this paper, a novel constrained Sparse Representation (SR) algorithm based on the joint sparsity and spatial correlation for hyperspectral image (HSI) classification is proposed. The coefficients in the sparse vector associated with the training samples in the structured dictionary exhibit the group sparsity continuity. However, this joint sparsity of the coefficient vector is not considered in the classical SR classifiers. In addition, spatial correlation has positive effect on HSI classification processing. Thus in the proposed SR model, we consider a joint sparsity regularization term to promote the joint sparsity of the sparse vectors and use space regularization to restrict spatial correlation of the output. The formulated problem is solved via the alternating direction method of multipliers (ADMM). Simulation results show that the proposed algorithm has the improved performance.
关键词： sparse representation,classification,Hyperspectral imagery,joint sparsity,ADMM
更新于2025-09-23 15:22:29
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Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery
摘要： Dimensionality Reduction (DR) models are of signi?cance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we ?rstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but ef?cient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs ef?ciently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.
关键词： hyperspectral imagery,graph embedding,dimensionality reduction,collaborative representation,discriminant analysis
更新于2025-09-23 15:22:29
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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 - Hyperspectral Compressive Sensing on Low Energy Consumption Board
摘要： Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands (at different wavelength channels) for the same area of the Earth surface. The acquired data cube comprises several GBs per flight, which have attracted attention to onboard compression techniques. Typically these compression techniques are expensive from the computational point of view. This paper presents a compressive sensing method implementation on a low power consumption Graphic Processing Unit. The experiments are conducted on a Jetson TX1 board, which is well suited to perform vector operations such as dot products. These experiments have been performed to demonstrate the applicability, in terms of accuracy and time consuming, of these methods for onboard processing. The results show that by using this low power consumption GPU it is possible to obtain real-time performance with a very limited power requirements.
关键词： GPU,Low-Power Consumption,Hyperspectral Imagery,Compressive Sensing
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE Power & Energy Society General Meeting (PESGM) - Atlanta, GA, USA (2019.8.4-2019.8.8)] 2019 IEEE Power & Energy Society General Meeting (PESGM) - A New Directional Algorithm-based Approach to Fault Localization for Distribution Networks with High Penetration of Photovoltaic Systems
摘要： The feasibility study of the HALESIS (High-Altitude Luminous Events Studied by Infrared Spectro-imagery) project is presented. The purpose of this experiment is to measure the atmospheric perturbation in the minutes following the occurrence of transient luminous events (TLEs) from a stratospheric balloon in the altitude range of 20–40 km. The instrumentation will include a spectro-imager embedded in a pointing gondola. Infrared signatures of a single blue jet were simulated under the assumption of local thermodynamic equilibrium (LTE), and were then compared with a panel of commercially available instrument specifications. The sensitivity of the signatures with a local perturbation of the main vibrational energy level populations of CO2, CO, NO, O3, and H2O was measured and the infrared signatures of a single blue jet taking into account non-LTE hypotheses were compared with the same panel of commercially available instrument specifications. Lastly, the feasibility of the study is discussed.
关键词： transient luminous events (TLEs),hyperspectral imagery,Atmospheric chemistry
更新于2025-09-19 17:13:59