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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Multipixel Anomaly Detection With Unknown Patterns for Hyperspectral Imagery.

Jun Liu, Zengfu Hou, Wei Li

IEEE Transactions on Neural Networks and Learning Systems

|April 14, 2021

Summary

This summary is machine-generated.

This study introduces two adaptive detectors for hyperspectral imagery anomaly detection, offering a constant false alarm rate and improved performance over existing methods for detecting multi-pixel anomalies.

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Area of Science:

Remote Sensing
Signal Processing
Data Analysis

Background:

Anomaly detection in hyperspectral imagery is crucial for identifying targets of interest.
Existing methods often struggle with unknown background covariance and multi-pixel anomalies.

Purpose of the Study:

To develop and evaluate novel adaptive detectors for hyperspectral anomaly detection.
To address challenges posed by Gaussian background with unknown covariance and unknown anomaly patterns.

Main Methods:

Generalized Likelihood Ratio Test (GLRT) design procedure.
Ad hoc modification of the GLRT for adaptive detection.
Derivation of analytical expressions for the probability of false alarm.

Main Results:

Two proposed adaptive detectors were found to be equivalent.
The detector exhibits a constant false alarm rate (CFAR) against the noise covariance matrix.
Performance is influenced by background data size and pixel number.

Conclusions:

The proposed detector demonstrates superior detection performance compared to counterparts on real hyperspectral data.
The developed method offers a robust solution for multi-pixel anomaly detection in hyperspectral imaging.