Document Type

Article

Publication Date

2-2016

Publication Source

IEEE Transactions on Geoscience and Remote Sensing

Abstract

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object's spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task.

The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches.

Inclusive pages

1196-1208

ISBN/ISSN

0196-2892

Document Version

Postprint

Comments

The document available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file.

Some differences may exist between this version and the published version; as such, researchers wishing to quote directly from this source are advised to consult the version of record.

Publisher

IEEE

Volume

54

Issue

2

Peer Reviewed

yes

Link to published version

Share

COinS