Accession Number:

ADA576754

Title:

Hyperspectral Detection and Discrimination Using the ACE Algorithm

Descriptive Note:

Rept. for Aug-Sep 2011

Corporate Author:

MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB

Personal Author(s):

• Pieper, M. L.
• Manolakis, D.
• Lockwood, R.
• Cooley, T.
• Armstrong, P.
• Jacobson, J.

Report Date:

2011-08-08

Pagination or Media Count:

13.0

Abstract:

One of the fundamental challenges for a hyperspectral imaging system is the detection and discrimination of subpixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection and discrimination. In many applications we look for a single signature and discrimination among different signatures is not required. However, there are important applications where we are interested for multiple signatures. In these cases, the use of spectral discrimination algorithms is both necessary and valuable. In this paper, we develop an approach to spectral discrimination based on the adaptive cosine estimation ACE algorithm. The basic idea is to jointly exploit the detection statistics from the various signatures and set a common threshold that ensures larger separation between signatures of interest and background. The operation of the proposed detection-discrimination approach is illustrated using real-world hyperspectral imaging data.

Descriptors:

• *ALGORITHMS
• *HYPERSPECTRAL IMAGERY
• DISCRIMINATION
• MATCHED FILTERS
• SPECTROMETERS
• TARGET DETECTION

Subject Categories:

• Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE