By Topic

Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Chein-I Chang ; Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland, Baltimore, MD, USA

The orthogonal subspace projection (OSP) approach has received considerable interest in hyperspectral data exploitation recently. It has been shown to be a versatile technique for a wide range of applications. Unfortunately, insights into its design rationale have not been investigated and have yet to be explored. This work conducts a comprehensive study and analysis on the OSP from several signal processing perspectives and further discusses in depth how to effectively operate the OSP using different levels of a priori target knowledge for target detection and classification. Additionally, it looks into various assumptions made in the OSP and analyzes filters with different forms, some of which turn out to be well-known and popular target detectors and classifiers. It also shows how the OSP is related to the well-known least-squares-based linear spectral mixture analysis and how the OSP takes advantage of Gaussian noise to arrive at the Gaussian maximum-likelihood detector/estimator and likelihood ratio test. Extensive experiments are also included in this paper to simulate various scenarios to illustrate the utility of the OSP operating under various assumptions and different degrees of target knowledge.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:43 ,  Issue: 3 )