NDLI: An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection

Content Provider	IEEE Xplore Digital Library
Author	Matteoli, S. Acito, N. Diani, M. Corsini, G.
Copyright Year	1980
Abstract	This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	790
Ending Page	800
Page Count	11
File Size	1137174
File Format	PDF
ISSN	01962892
Volume Number	49
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Estimation Hyperspectral imaging Covariance matrix Detectors Complexity theory target detection Background estimation background suppression orthogonal projection subspace-based detection
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Local approach to orthogonal subspace-based target detection in hyperspectral images

A new algorithm for local background suppression in hyperspectral target detection

Kernel orthogonal subspace projection for hyperspectral signal classification

A noise subspace projection approach to target signature detection and extraction in an unknown background for hyperspectral images

Comments on Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis

Analytical Comparison of the Matched Filter and Orthogonal Subspace Projection Detectors for Hyperspectral Images

Target Detection With Semisupervised Kernel Orthogonal Subspace Projection

Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation

Asymptotically CFAR-Unsupervised Target Detection and Discrimination in Hyperspectral Images With Anomalous-Component Pursuit

An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection

Similar Documents

Local approach to orthogonal subspace-based target detection in hyperspectral images

A new algorithm for local background suppression in hyperspectral target detection

Kernel orthogonal subspace projection for hyperspectral signal classification

A noise subspace projection approach to target signature detection and extraction in an unknown background for hyperspectral images

Comments on Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis

Analytical Comparison of the Matched Filter and Orthogonal Subspace Projection Detectors for Hyperspectral Images

Target Detection With Semisupervised Kernel Orthogonal Subspace Projection

Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation

Asymptotically CFAR-Unsupervised Target Detection and Discrimination in Hyperspectral Images With Anomalous-Component Pursuit

An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection