Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing

• URL: http://arxiv.org/abs/2001.00425v2
• Date: Wed, 12 Aug 2020 21:52:20 GMT
• Title: Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Abstract summary: We propose a spectral unmixing (SU) strategy using physically motivated endmember representations to account for temporal spectral variability. By representing the multitemporal mixing process using a state-space formulation, we are able to exploit the filtering machinery to estimate endmember variability coefficients. Simulation results indicate that the proposed strategy outperforms state-of-the-art multitemporal SU algorithms.
• Score: 20.404244393774253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images. In this work, we propose a novel spectral unmixing (SU) strategy using physically motivated parametric endmember representations to account for temporal spectral variability. By representing the multitemporal mixing process using a state-space formulation, we are able to exploit the Bayesian filtering machinery to estimate the endmember variability coefficients. Moreover, by assuming that the temporal variability of the abundances is small over short intervals, an efficient implementation of the expectation maximization (EM) algorithm is employed to estimate the abundances and the other model parameters. Simulation results indicate that the proposed strategy outperforms state-of-the-art multitemporal SU algorithms.

Related papers

• FRACTAL: SSM with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences [13.320173396455226]
  This paper proposes a Fractional Recurrent Architecture for Computational Temporal Analysis of Long sequences (FRACTAL)<n>By deriving projection operators with analytically characterized spectral properties and a tunable index, the proposed method sensitivity to recent signal perturbations while preserving the spectral structure that encodes fractional scale-invariant memory dynamics.<n>FRACTAL achieves an average score of 87.11% on the Long Range Arena benchmark, including 61.85% on the ListOps task, outperforming the S5 model.
  arXiv  Detail & Related papers  (2026-05-09T09:38:44Z)
• Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery [2.8516007089651043]
  Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances)<n>This study develops a novel approach called sparsity and total variation (TV) constrained multilayer linear unmixing (STVMLU) for hyperspectral imagery.
  arXiv  Detail & Related papers  (2025-08-05T12:50:55Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows [0.0]
  We introduce the framework of multi-observable dynamic mode decomposition (MODMD) We replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.
  arXiv  Detail & Related papers  (2024-09-20T17:59:56Z)
• Multi-scale Conditional Generative Modeling for Microscopic Image Restoration [9.345394120765123]
  We introduce a multi-scale generative model that enhances conditional image restoration through a novel exploitation of the Brownian Bridge process within wavelet domain. Our method provides significant acceleration during training and sampling while sustaining a high image generation quality and diversity on par with SOTA diffusion models. This pioneering technique offers an efficient image restoration framework that harmonizes efficiency with quality, signifying a major stride in incorporating cutting-edge generative models into computational microscopy.
  arXiv  Detail & Related papers  (2024-07-07T05:11:00Z)
• TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models [40.5153344875351]
  We introduce TMPQ-DM, which jointly optimize timestep reduction and quantization to achieve a superior performance-efficiency trade-off. For timestep reduction, we devise a non-uniform grouping scheme tailored to the non-uniform nature of the denoising process. In terms of quantization, we adopt a fine-grained layer-wise approach to allocate varying bit-widths to different layers based on their respective contributions to the final generative performance.
  arXiv  Detail & Related papers  (2024-04-15T07:51:40Z)
• Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting [49.1574468325115]
  We present a novel feature selection method embedded in Long Short-Term Memory networks. Our approach optimize the weights and biases of the LSTM in a partitioned manner. Experimental evaluations on air quality time series data from Italy and southeast Spain demonstrate that our method substantially improves the ability generalization of conventional LSTMs.
  arXiv  Detail & Related papers  (2023-12-29T08:42:10Z)
• Momentum Particle Maximum Likelihood [2.4561590439700076]
  We propose an analogous dynamical-systems-inspired approach to minimizing the free energy functional. By discretizing the system, we obtain a practical algorithm for Maximum likelihood estimation in latent variable models. The algorithm outperforms existing particle methods in numerical experiments and compares favourably with other MLE algorithms.
  arXiv  Detail & Related papers  (2023-12-12T14:53:18Z)
• Towards Accurate Post-training Quantization for Diffusion Models [73.19871905102545]
  We propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation. Our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost.
  arXiv  Detail & Related papers  (2023-05-30T04:00:35Z)
• Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation [2.53740603524637]
  We develop a class of interacting particle systems for implementing a maximum marginal likelihood estimation procedure. In particular, we prove that the parameter marginal of the stationary measure of this diffusion has the form of a Gibbs measure. Using a particular rescaling, we then prove geometric ergodicity of this system and bound the discretisation error. in a manner that is uniform in time and does not increase with the number of particles.
  arXiv  Detail & Related papers  (2023-03-23T16:50:08Z)
• Moment evolution equations and moment matching for stochastic image EPDiff [68.97335984455059]
  Models of image deformation allow study of time-continuous effects transforming images by deforming the image domain. Applications include medical image analysis with both population trends and random subject specific variation. We use moment approximations of the corresponding Ito diffusion to construct estimators for statistical inference in the parameters full model.
  arXiv  Detail & Related papers  (2021-10-07T11:08:11Z)
• 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)
• STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation [64.99259320624148]
  We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015.
  arXiv  Detail & Related papers  (2020-07-10T17:01:34Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

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.