Anomaly detection and compensation for hyperspectral imagery

Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far. A generic methodology is presented for detecting and compensating anomalies from hyperspectral imagery, taking advantage of all information available -- spectral and spatial correlation and any a priori knowledge about the anomalies. An anomaly is generally defined as an undesired spatial and spectral feature statistically different from its surrounding background. Principal component analysis (PCA) and the Iterative Order and Noise (ION) estimation algorithm provide valuable tools to characterize signals and reduce noise. Various methodologies are also addressed to cope with nonlinearities in the system without much computational burden. An anomaly compensation technique is applied to specific problems that exhibit different stochastic models for an anomaly and its performance is evaluated.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Cho, Choongyeun, 1973-

Advisor dc:contributor.advisor

• David H. Staelin.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/34980

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34980