Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection

2026-01-18

AI Summary

This work addresses the challenge of hyperspectral anomaly detection, where complex distributions of background and anomalies in high-dimensional spectral space and the absence of labeled data hinder performance. The study introduces score-based generative models (SGMs) to this task for the first time, leveraging the low-dimensional manifold assumption inherent in hyperspectral data. By estimating the gradient field—i.e., the score—of the data distribution through spectral perturbation kernels, the method effectively distinguishes pixels conforming to the background manifold from anomalous targets that deviate from it. Notably, the approach operates in a fully unsupervised manner, requiring no anomalous samples during training. Experimental results on four standard hyperspectral datasets demonstrate significant superiority over existing unsupervised anomaly detection methods.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• hyperspectral anomaly detection
• manifold hypothesis
• score-based generative model
• data distribution
• anomaly detection

Innovation

• Score-based Generative Model
• Hyperspectral Anomaly Detection
• Manifold Hypothesis
• Score Function
• Unsupervised Detection