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A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery.

Xiurui Geng¹, Kang Sun¹, Luyan Ji²

¹Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.

Scientific Reports

|November 5, 2014

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This study introduces the coskewness detector (COSD) for hyperspectral anomaly detection. COSD offers a single-map output without iterations, improving upon existing high-order statistics methods.

Area of Science:

Remote Sensing
Signal Processing
Data Analysis

Background:

High-order statistics are increasingly used for hyperspectral anomaly detection.
Current methods often require iterative processes and yield multiple detection maps, complicating analysis.

Purpose of the Study:

To propose a novel, non-iterative hyperspectral anomaly detection method.
To develop a detector that generates a single anomaly distribution map.

Main Methods:

Exploitation of the coskewness tensor concept.
Development of the coskewness detector (COSD).

Main Results:

COSD effectively detects anomalies in hyperspectral data.
The method produces a single, clear anomaly distribution map.

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COSD eliminates the need for stepwise iterations.

Conclusions:

The proposed coskewness detector (COSD) is an effective algorithm for hyperspectral anomaly detection.
COSD offers advantages over existing iterative methods by providing a single detection map.