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Hyperspectral target detection via discrete wavelet-based spectral fringe-adjusted joint transform correlation.

Adel A Sakla¹, Wesam A Sakla, Mohammad S Alam

¹Department of Electrical and Computer Engineering, University of South Alabama, Mobile, Alabama 36688, USA. asakla@usouthal.edu

|October 22, 2011

Summary

This study enhances hyperspectral target detection by using discrete wavelet transform (DWT) coefficients within the spectral fringe-adjusted joint transform correlation (SFJTC) method. This approach significantly improves insensitivity to spectral variability, leading to better detection accuracy.

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Area of Science:

Remote Sensing
Signal Processing
Computer Vision

Background:

Spectral variability in hyperspectral imagery (HSI) presents a significant challenge for accurate target detection.
The spectral fringe-adjusted joint transform correlation (SFJTC) is a recognized technique for HSI target detection.
Existing methods often struggle with variations in spectral signatures due to environmental factors.

Purpose of the Study:

To develop a more robust hyperspectral target detection method by reducing sensitivity to spectral variability.
To integrate discrete wavelet transform (DWT) coefficients into the SFJTC framework for enhanced feature representation.
To evaluate the performance of the proposed DWT-based SFJTC technique under diverse spectral variability conditions.

Main Methods:

Feature extraction using discrete wavelet transform (DWT) coefficients of spectral signatures.
Development of a supervised training algorithm to select optimal DWT coefficients.
Implementation of the DWT-based SFJTC technique for hyperspectral target detection.
Performance evaluation using simulated HSI data with inserted targets in urban and vegetative scenes.

Main Results:

The proposed DWT-based SFJTC technique demonstrated superior performance compared to SFJTC using original signatures.
Receiver-operating-characteristic (ROC) and area-under-the-ROC (AUROC) curves indicated improved detection accuracy.
The method achieved the largest mean AUROC values across various operating conditions with differing spectral variability.

Conclusions:

Discrete wavelet transform coefficients effectively enhance the robustness of SFJTC for hyperspectral target detection.
The proposed DWT-based SFJTC method offers a significant improvement in handling spectral variability.
This approach provides a more reliable solution for target detection in challenging hyperspectral imaging applications.