SPIE Proceedings

2008

DOI: 10.1117/12.776550

|View full text |Cite

|

Sign up to set email alerts

|

Deterministic hyperspectral target detection using the DWT and spectral fringe-adjusted joint transform correlation

¹

,

²

,

Mohammad S. Alam

³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

0

Mentioning

1

Contrasting

0

Year Published

2009

2009

2012

2012

Publication Types

Select...

Other1

Book1

Article1

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

0

Mentioning

1

Contrasting

0

Order By: Relevance

“…In this work, we expand upon prior work that shows that use of the discrete wavelet transform (DWT) coefficients can lead to improved ROC curve behavior in hyperspectral target detection using the SFJTC technique 3,4 . The improvement in detection performance is proportional to the degradation of the signatures in the scene.…”

Section: Introductionmentioning

confidence: 95%

Wavelet-based hyperspectral target detection using spectral fringe-adjusted joint transform correlation

¹

,

²

,

³

2009

Automatic Target Recognition XIX

Self Cite

View full text Add to dashboard Cite

Recently, the 1-D spectral fringe-adjusted joint transform correlation (SFJTC) technique has been combined with the discrete wavelet transform (DWT) as an effective means for providing robust target detection in hyperspectral imagery. This paper expands upon earlier work that demonstrates the utility of the DWT in conjunction with SFJTC for detection. We show that using selected DWT coefficients at a given decomposition level can significantly improve the ROC curve behavior of the detection process in comparison to using the original hyperspectral signatures. The DWT coefficients that are selected for detection are based on a supervised training process that uses the pure target signature and randomly selected samples from the scene. We illustrate this by conducting experiments on two different hyperspectral scenes containing varying amounts of simulated noise. Results show that use of the selected DWT coefficients significantly improves the ROC curve detection behavior in the presence of noise.

“…In this work, we expand upon prior work that shows that use of the discrete wavelet transform (DWT) coefficients can lead to improved ROC curve behavior in hyperspectral target detection using the SFJTC technique 3,4 . The improvement in detection performance is proportional to the degradation of the signatures in the scene.…”

Section: Introductionmentioning

confidence: 95%

Wavelet-based hyperspectral target detection using spectral fringe-adjusted joint transform correlation

¹

,

²

,

³

2009

Automatic Target Recognition XIX

Self Cite

View full text Add to dashboard Cite

Recently, the 1-D spectral fringe-adjusted joint transform correlation (SFJTC) technique has been combined with the discrete wavelet transform (DWT) as an effective means for providing robust target detection in hyperspectral imagery. This paper expands upon earlier work that demonstrates the utility of the DWT in conjunction with SFJTC for detection. We show that using selected DWT coefficients at a given decomposition level can significantly improve the ROC curve behavior of the detection process in comparison to using the original hyperspectral signatures. The DWT coefficients that are selected for detection are based on a supervised training process that uses the pure target signature and randomly selected samples from the scene. We illustrate this by conducting experiments on two different hyperspectral scenes containing varying amounts of simulated noise. Results show that use of the selected DWT coefficients significantly improves the ROC curve detection behavior in the presence of noise.

Automatic Target Recognition in Multispectral and Hyperspectral Imagery Via Joint Transform Correlation

¹

,

²

2012

Augmented Vision and Reality

View full text Add to dashboard Cite

No abstract

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

¹

,

²

,

³

2011

View full text Add to dashboard Cite

Spectral variability remains a major challenge for target detection in hyperspectral imagery (HSI). Recently, the spectral fringe-adjusted joint transform correlation (SFJTC) technique has been used effectively for hyperspectral target detection applications. In this paper, we propose to use discrete wavelet transform (DWT) coefficients of the signatures as features for detection in order to make the SFJTC technique more insensitive to spectral variability. We devised a supervised training algorithm that uses the pure target signature and randomly selected samples from input scenery to select an optimal set of DWT coefficients for detection. We have inserted target signatures into urban and vegetative hyperspectral scenery with varying levels of spectral variability to explore the performance of our DWT-based SFJTC technique in different operating conditions. Detection results in the form of receiver-operating-characteristic (ROC) curves and area-under-the-ROC (AUROC) curves show that the proposed scheme yields the largest mean AUROC values compared to SFJTC using the original signatures and traditional hyperspectral detection algorithms.

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product

Browser Extension Assistant by scite Citation Statement Search Reference Check Visualizations Dashboards Explore Journals Explore Organizations Explore Funders Embedding Badge Embedding Citation Search Pricing

Resources

Blog Help & FAQ Accessibility Statement API Terms For Universities & Governments For Researchers For Publishers For Corporate, Pharma & Enterprise Author Marketing Become an Affiliate Get an organization trial or quote scite Data & Services

About

News & Press Careers Read our Paper Coverage

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Copyright © 2025 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.