Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces graph frequency analysis for hyperspectral anomaly detection (HAD). The novel approach effectively integrates graph structure and spectral features, improving anomaly detection performance in hyperspectral images (HSIs).
Area Of Science
- Remote Sensing
- Computer Vision
- Graph Theory
Background
- Graph theory methods are used for hyperspectral anomaly detection (HAD).
- Existing graph-based methods overemphasize graph structure and neglect spectral features in hyperspectral images (HSIs).
Purpose Of The Study
- To introduce graph frequency analysis for HAD.
- To integrate graph structure and spectral features effectively for improved anomaly detection.
Main Methods
- Constructing a beta distribution-based graph wavelet space for anomaly detection.
- Treating anomaly detection as a graph frequency location problem.
- Developing a novel entropy definition for adaptive frequency localization.
Main Results
- The proposed method leverages energy shifting to higher frequencies caused by anomalies.
- Accurate extraction of anomalies by pinpointing specific Beta wavelets associated with high-frequency content.
- Experimental validation on seven real HSIs demonstrated superior performance compared to state-of-the-art methods.
Conclusions
- Graph frequency analysis offers a powerful approach for HAD.
- The novel method effectively combines graph structure and spectral information.
- The approach shows significant improvements in detecting anomalies in hyperspectral images.
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