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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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Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
摘要： Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have proven that both global and local facial features play an important role in face recognition. This research proposed a novel local features extraction algorithm for hyperspectral facial images using local patch based low-rank tensor decomposition that also preserves the neighborhood relationship and spectral dimension information. Additionally, global contour features were extracted using the polar discrete fast Fourier transform (PFFT) algorithm, which addresses many challenges relevant to human face recognition such as illumination, expression, asymmetrical (orientation), and aging changes. Furthermore, an ensemble classifier was developed by combining the obtained local and global features. The proposed method was evaluated by using the Poly-U Database and was compared with other existing hyperspectral face recognition algorithms. The illustrative numerical results demonstrate that the proposed algorithm is competitive with the best CRC_RLS and PLS methods.
关键词： spectral and spatial information,polar discrete fast Fourier transform,band fusion,ensemble classifier,global and local features,tensor decomposition,hyperspectral images
更新于2025-09-23 15:22:29
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[Communications in Computer and Information Science] Advances in Signal Processing and Intelligent Recognition Systems Volume 968 (4th International Symposium SIRS 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers) || Pre-processed Hyperspectral Image Analysis Using Tensor Decomposition Techniques
摘要： Hyperspectral remote sensing image analysis has always been a challenging task and hence there are several techniques employed for exploring the images. Recent approaches include visualizing hyperspectral images as third order tensors and processing using various tensor decomposition methods. This paper focuses on behavioural analysis of hyperspectral images processed with various decompositions. The experiments includes processing raw hyperspectral image and pre-processed hyperspectral image with tensor decomposition methods such as, Multilinear Singular Value Decomposition and Low Multilinear Rank Approximation technique. The results are projected based on relative reconstruction error, classification and pixel reflectance spectrums. The analysis provides correlated experimental results, which emphasizes the need of pre-processing for hyperspectral images and the trend followed by the tensor decomposition methods.
关键词： Low Multilinear Rank Approximation,Remote sensing image,Pixel reflectance spectrums,Multilinear Singular Value Decomposition,Relative reconstruction error,Tensor decomposition
更新于2025-09-19 17:15:36
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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 - Deep Tensor Factorization for Hyperspectral Image Classification
摘要： High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral datasets show the superiority of the proposed method.
关键词： Hyperspectral image (HSI) classification,feature extraction,convolutional neural network (CNN),tensor decomposition
更新于2025-09-10 09:29:36
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Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection
摘要： Night-time vehicle detection is essential in building intelligent transportation systems (ITS) for road safety. Most of current night-time vehicle detection approaches focus on one or two classes of vehicles. In this paper, we present a novel multi-class vehicle detection system based on tensor decomposition and object proposal. Commonly used features such as histogram of oriented gradients and local binary pattern often produce useless image blocks (regions), which can result in unsatisfactory detection performance. Thus, we select blocks via feature ranking after tensor decomposition and only extract features from these selected blocks. To generate windows that contain all vehicles, we propose a novel object-proposal approach based on a state-of-the-art object-proposal method, local features, and image region similarity. The three terms are summed with learned weights to compute the reliability score of each proposal. A bio-inspired image enhancement method is used to enhance the brightness and contrast of input images. We have built a Hong Kong night-time multiclass vehicle dataset for evaluation. Our proposed vehicle detection approach can successfully detect four types of vehicles: 1) car; 2) taxi; 3) bus; and 4) minibus. Occluded vehicles and vehicles in the rain can also be detected. Our proposed method obtains 95.82% detection rate at 0.05 false positives per image, and it outperforms several state-of-the-art night-time vehicle detection approaches.
关键词： tensor decomposition,object proposal,Feature selection,night-time multiclass vehicle detection
更新于2025-09-10 09:29:36
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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 - Spatial Resolution Enhancement of Optical Images Based on Tensor Decomposition
摘要： There is an inevitable trade-off between spatial and spectral resolutions in optical remote sensing images. A number of data fusion techniques of multimodal images with different spatial and spectral characteristics have been developed to generate optical images with both spatial and spectral high resolution. Although some of the techniques take the spectral and spatial blurring process into account, there is no method that attempts to retrieve an optical image with both spatial and spectral high resolution, a spectral blurring filter and a spectral response simultaneously. In this paper, we propose a new framework of spatial resolution enhancement by a fusion of multiple optical images with different characteristics based on tensor decomposition. An optical image with both spatial and spectral high resolution, together with a spatial blurring filter and a spectral response, is generated via canonical polyadic (CP) decomposition of a set of tensors. Experimental results featured that relatively reasonable results were obtained by regularization based on nonnegativity and coupling.
关键词： tensor decomposition,pan-sharpening,canonical polyadic (CP) decomposition,Spatial resolution enhancement,coupling
更新于2025-09-09 09:28:46
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[Lecture Notes in Computer Science] Smart Multimedia Volume 11010 (First International Conference, ICSM 2018, Toulon, France, August 24–26, 2018, Revised Selected Papers) || A Regularized Nonnegative Third Order Tensor decomposition Using a Primal-Dual Projected Gradient Algorithm: Application to 3D Fluorescence Spectroscopy
摘要： This paper investigates the use of Primal-Dual optimization algorithms on multidimensional signal processing problems. The data blocks interpreted in a tensor way can be modeled by means of multi-linear decomposition. Here we will focus on the Canonical Polyadic Decomposition (CPD), and we will present an application to fluorescence spectroscopy using this decomposition. In order to estimate the factors or latent variables involved in these decompositions, it is usual to use criteria optimization algorithms. A classical cost function consists of a measure of the modeling error (fidelity term) to which a regularization term can be added if necessary. Here, we consider one of the most efficient optimization methods, Primal-Dual Projected Gradient. The effectiveness and the robustness of the proposed approach are shown through numerical examples.
关键词： Primal-Dual,Projected gradient,Constrained optimization,Nonnegative tensor decomposition,Regularization
更新于2025-09-04 15:30:14