Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling

• URL: http://arxiv.org/abs/2512.09871v1
• Date: Wed, 10 Dec 2025 17:57:52 GMT
• Title: Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling
• Authors: Yimin Zhu, Lincoln Linlin Xu,
• Abstract summary: Linear spectral mixture models (LMM) provide a concise form to disentangle the constituent materials (endmembers) and their corresponding proportions (abundance) in a single pixel.<n>The critical challenges are how to model the spectral prior distribution and spectral variability.<n>Considering the key challenges and the advantages of the Bayesian framework, a novel method using a diffusion posterior sampler for semiblind unmixing is proposed.
• Score: 2.1741810474011047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Linear spectral mixture models (LMM) provide a concise form to disentangle the constituent materials (endmembers) and their corresponding proportions (abundance) in a single pixel. The critical challenges are how to model the spectral prior distribution and spectral variability. Prior knowledge and spectral variability can be rigorously modeled under the Bayesian framework, where posterior estimation of Abundance is derived by combining observed data with endmember prior distribution. Considering the key challenges and the advantages of the Bayesian framework, a novel method using a diffusion posterior sampler for semiblind unmixing, denoted as DPS4Un, is proposed to deal with these challenges with the following features: (1) we view the pretrained conditional spectrum diffusion model as a posterior sampler, which can combine the learned endmember prior with observation to get the refined abundance distribution. (2) Instead of using the existing spectral library as prior, which may raise bias, we establish the image-based endmember bundles within superpixels, which are used to train the endmember prior learner with diffusion model. Superpixels make sure the sub-scene is more homogeneous. (3) Instead of using the image-level data consistency constraint, the superpixel-based data fidelity term is proposed. (4) The endmember is initialized as Gaussian noise for each superpixel region, DPS4Un iteratively updates the abundance and endmember, contributing to spectral variability modeling. The experimental results on three real-world benchmark datasets demonstrate that DPS4Un outperforms the state-of-the-art hyperspectral unmixing methods.

Related papers

• Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling [63.87118366026116]
  In signal processing, the estimation of components of interest is a crucial pre-processing step.<n>We develop a framework for a signal component decomposition, which combines Gibbs sampling with plug-and-play ( plug-and-play ( DiG)<n>We show that DiG can be interpreted as an extension of a class recently proposed diffusion-based samplers, and that, for suitable classes of sensing operators, DiG better exploits the structure of the measurement structure.
  arXiv  Detail & Related papers  (2026-02-11T12:34:37Z)
• SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for Pan-Sharpening [50.44337053599724]
  SALAD-Pan is a sensor-agnostic latent space diffusion method for efficient pansharpening.<n>It trains a band-wise single-channel VAE to encode high-resolution multispectral images into compact latent representations.<n>It achieves high-precision fusion in the diffusion process and robust zero-shot (cross-sensor) capability.
  arXiv  Detail & Related papers  (2026-02-04T12:01:07Z)
• Latent Dirichlet Transformer VAE for Hyperspectral Unmixing with Bundled Endmembers [1.9336815376402718]
  We propose the Latent Dirichlet Transformer Variational Autoencoder (LDVAE-T) for hyperspectral unmixing.<n>Our model combines the global context modeling capabilities of transformer architectures with physically meaningful constraints imposed by a Dirichlet prior in the latent space.<n>We evaluate our approach on three benchmark datasets, Samson, Jasper Ridge, and HYDICE Urban.
  arXiv  Detail & Related papers  (2025-11-21T20:15:37Z)
• Spectral Dictionary Learning for Generative Image Modeling [0.0]
  We propose a novel spectral generative model for image synthesis.<n>Images are reconstructed as linear combinations of a set of learned spectral basis functions.<n>We show that our approach achieves competitive performance in terms of reconstruction quality and perceptual fidelity.
  arXiv  Detail & Related papers  (2025-04-21T01:11:17Z)
• DPBridge: Latent Diffusion Bridge for Dense Prediction [49.1574468325115]
  We introduce DPBridge, the first latent diffusion bridge framework for dense prediction tasks.<n>Our method consistently achieves superior performance, demonstrating its effectiveness and capability generalization under different scenarios.
  arXiv  Detail & Related papers  (2024-12-29T15:50:34Z)
• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• Boosting Diffusion Models with Moving Average Sampling in Frequency Domain [101.43824674873508]
  Diffusion models rely on the current sample to denoise the next one, possibly resulting in denoising instability. In this paper, we reinterpret the iterative denoising process as model optimization and leverage a moving average mechanism to ensemble all the prior samples. We name the complete approach "Moving Average Sampling in Frequency domain (MASF)"
  arXiv  Detail & Related papers  (2024-03-26T16:57:55Z)
• Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction [31.503662384666274]
  In science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain imaging modality. Motivated Score-based diffusion models, due to its empirical success, have emerged as an impressive candidate of an exemplary prior in image reconstruction.
  arXiv  Detail & Related papers  (2024-03-25T15:58:26Z)
• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
  Iterated Denoising Energy Matching (iDEM) iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
  arXiv  Detail & Related papers  (2024-02-09T01:11:23Z)
• Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer [14.594398447576188]
  In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling. In our experiments, we found that the high accuracy was reached because the training and test datasets share a lot of information. We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, that preserves generalization across dataset partitions.
  arXiv  Detail & Related papers  (2023-06-29T07:55:43Z)
• DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model [46.68717345017946]
  Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing. We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD) Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
  arXiv  Detail & Related papers  (2023-05-21T09:21:41Z)

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.