# Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection

arXiv: https://arxiv.org/abs/2601.12379 · HTML: https://mlsanity.com/paper/2601.12379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [GAN-based Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2007.02441/) (2020)
- [BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2307.09861/) (2023)
- [Graph Laplacian for Image Anomaly Detection](https://mlsanity.com/paper/1802.09843/) (2018)
- [Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance](https://mlsanity.com/paper/1606.03838/) (2016)

## Built on by (most cited first, 1 of 1)

- [Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2604.11390/) (2026)

## Abstract (arXiv)

Hyperspectral images (HSIs) are a type of image that contains abundant spectral information. As a type of real-world data, the high-dimensional spectra in hyperspectral images are actually determined by only a few factors, such as chemical composition and illumination. Thus, spectra in hyperspectral images are highly likely to satisfy the manifold hypothesis. Based on the hyperspectral manifold hypothesis, we propose a novel hyperspectral anomaly detection method (named ScoreAD) that leverages the time-dependent gradient field of the data distribution (i.e., the score), as learned by a score-based generative model (SGM). Our method first trains the SGM on the entire set of spectra from the hyperspectral image. At test time, each spectrum is passed through a perturbation kernel, and the resulting perturbed spectrum is fed into the trained SGM to obtain the estimated score. The manifold hypothesis of HSIs posits that background spectra reside on one or more low-dimensional manifolds. Conversely, anomalous spectra, owing to their unique spectral signatures, are considered outliers that do not conform to the background manifold. Based on this fundamental discrepancy in their manifold distributions, we leverage a generative SGM to achieve hyperspectral anomaly detection. Experiments on the four hyperspectral datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/jiahuisheng/ScoreAD.