Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection

• URL: http://arxiv.org/abs/2503.05183v1
• Date: Fri, 07 Mar 2025 07:08:14 GMT
• Title: Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection
• Authors: Quan Yu, Yu-Hong Dai, Minru Bai,
• Abstract summary: Low rank tensor representation (LRTR) methods are very useful for hyperspectral anomaly detection (HAD)<n>We first apply non-negative matrix factorization (NMF) to alleviate spectral dimensionality redundancy and extract spectral anomaly.<n>We then employ LRTR to extract spatial anomaly while mitigating spatial redundancy, yielding a highly efffcient layered tensor decomposition framework for HAD.<n> Experimental results on the Airport-Beach-Urban and MVTec datasets demonstrate that our approach outperforms state-of-the-art methods in the HAD task.
• Score: 6.292153194561472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low rank tensor representation (LRTR) methods are very useful for hyperspectral anomaly detection (HAD). To overcome the limitations that they often overlook spectral anomaly and rely on large-scale matrix singular value decomposition, we first apply non-negative matrix factorization (NMF) to alleviate spectral dimensionality redundancy and extract spectral anomaly and then employ LRTR to extract spatial anomaly while mitigating spatial redundancy, yielding a highly efffcient layered tensor decomposition (LTD) framework for HAD. An iterative algorithm based on proximal alternating minimization is developed to solve the proposed LTD model, with convergence guarantees provided. Moreover, we introduce a rank reduction strategy with validation mechanism that adaptively reduces data size while preventing excessive reduction. Theoretically, we rigorously establish the equivalence between the tensor tubal rank and tensor group sparsity regularization (TGSR) and, under mild conditions, demonstrate that the relaxed formulation of TGSR shares the same global minimizers and optimal values as its original counterpart. Experimental results on the Airport-Beach-Urban and MVTec datasets demonstrate that our approach outperforms state-of-the-art methods in the HAD task.

Related papers

• Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation [71.69331824668954]
  Spectral variations pose a common challenge in analyzing hyperspectral images (HSI) Low-rank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. We propose a novel model for irregular tensor lowrank representation tailored to efficiently model irregular 3D cubes.
  arXiv  Detail & Related papers  (2024-10-24T02:56:22Z)
• Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior [29.233195935103172]
  We propose a self-supervised network called self-supervised anomaly prior (SAP) for hyperspectral anomaly detection. SAP offers a more accurate and interpretable solution than other advanced HAD methods. In addition, a dual-purified strategy is proposed to provide a more refined background representation with an enriched background dictionary.
  arXiv  Detail & Related papers  (2024-04-20T10:40:12Z)
• Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition [26.76968345244154]
  We study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation.
  arXiv  Detail & Related papers  (2023-10-16T04:02:34Z)
• Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems [64.29491112653905]
  We propose a novel and efficient diffusion sampling strategy that synergistically combines the diffusion sampling and Krylov subspace methods. Specifically, we prove that if tangent space at a denoised sample by Tweedie's formula forms a Krylov subspace, then the CG with the denoised data ensures the data consistency update to remain in the tangent space. Our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method.
  arXiv  Detail & Related papers  (2023-03-10T07:42:49Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)
• Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization [49.55649406434796]
  We propose a novel non particular approach to robust principal component analysis for HSI denoising. We develop accurate approximations to both rank and sparse components. Experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-01-08T11:48:46Z)
• Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization [10.131033322742363]
  We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Experiments demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.
  arXiv  Detail & Related papers  (2021-11-13T05:30:56Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography [20.03088101097943]
  Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs) Due to the limited photons and counting rate in the corresponding spectral fraction, the reconstructed spectral images usually suffer from severe noise. In this paper, a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR) is proposed.
  arXiv  Detail & Related papers  (2020-10-27T15:14:36Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)
• Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation [105.33409035876691]
  This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. We design a novel structured tensor low-rank norm tailored to MVSC. We show that the proposed method outperforms state-of-the-art methods to a significant extent.
  arXiv  Detail & Related papers  (2020-04-30T11:52:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.