Turbo-GoDec: Exploiting the Cluster Sparsity Prior for Hyperspectral Anomaly Detection

2026-01-18

AI Summary

This work addresses the limitation of existing hyperspectral anomaly detection methods that rely solely on pixel-wise sparsity priors, thereby neglecting the spatial clustering nature of anomalies and struggling to detect small targets effectively. To overcome this, the authors propose a novel approach that, for the first time, incorporates a cluster sparsity prior into the sparse decomposition step of the GoDec algorithm. Spatial contiguity of anomalies is modeled via a Markov random field, and marginal anomaly probabilities are computed through message passing on a factor graph to guide sparse component extraction. By seamlessly integrating cluster sparsity with the low-rank background decomposition framework, the method transcends the constraints of conventional sparsity assumptions. Experimental results on three real hyperspectral datasets demonstrate significant performance gains over the original GoDec (LSMAD) and state-of-the-art techniques, particularly excelling in the detection of small-sized anomalous targets.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Graph-based Machine Learning
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily relt on two prior assumption: low-rank background and sparse anomaly, along with additional spatial assumptions of the background. However, most methods only utilize the sparsity prior assumption for anomalies and rarely expand on this hypothesis. From observations of hyperspectral images, we find that anomalous pixels exhibit certain spatial distribution characteristics: they often manifest as small, clustered groups in space, which we refer to as cluster sparsity of anomalies. Then, we combined the cluster sparsity prior with the classical GoDec algorithm, incorporating the cluster sparsity prior into the S-step of GoDec. This resulted in a new hyperspectral anomaly detection method, which we called Turbo-GoDec. In this approach, we modeled the cluster sparsity prior of anomalies using a Markov random field and computed the marginal probabilities of anomalies through message passing on a factor graph. Locations with high anomalous probabilities were treated as the sparse component in the Turbo-GoDec. Experiments are conducted on three real hyperspectral image (HSI) datasets which demonstrate the superior performance of the proposed Turbo-GoDec method in detecting small-size anomalies comparing with the vanilla GoDec (LSMAD) and state-of-the-art anomaly detection methods. The code is available at https://github.com/jiahuisheng/Turbo-GoDec.

Problem

• hyperspectral anomaly detection
• cluster sparsity
• spatial distribution
• anomaly detection
• hyperspectral image

Innovation

• cluster sparsity
• hyperspectral anomaly detection
• GoDec
• Markov random field
• message passing