NDLI: New insights and practical considerations in hyperspectral change detection

Content Provider	IEEE Xplore Digital Library
Author	Pieper, M. Manolakis, D. Cooley, T. Brueggeman, M. Weisner, A. Jacobson, J.
Copyright Year	2015
Description	Author affiliation: Space Vehicles Directorate, Air Force Res. Lab., Kirtland AFB, NM, USA (Cooley, T.) \|\| Intell. Center, Nat. Air & Space, Wright-Patterson AFB, OH, USA (Brueggeman, M.; Weisner, A.; Jacobson, J.) \|\| MIT Lincoln Lab., Lexington, MA, USA (Pieper, M.; Manolakis, D.)
Abstract	There are a multitude of civilian and military applications for the detection of anomalous changes in hyperspectral images. Anomalous changes occur when the material within a pixel is replaced. Environmental factors that change over time, such as illumination, will affect the radiance of all the pixels in a scene, despite the materials within remaining constant. The goal of an anomalous change detection algorithm is to suppress changes caused by the environment, and detect pixels where the materials within have changed. Anomalous change detection is a two step process. Two co-registered images of a scene are first transformed to maximize the overall correlation between the images, then an anomalous change detector (ACD) is applied to the transformed images. The transforms maximize the correlation between the two images to attenuate the environmental differences that distract from the anomalous changes of importance. Several categories of transforms with different optimization parameters are discussed and compared. One of two types of ACDs are then applied to the transformed images. The first ACD uses the difference of the two transformed images. The second concatenates the spectra of two images and uses an aggregated ACD. A comparison of the two ACD methods and their effectiveness with the different transforms is done for the first time.
Starting Page	4161
Ending Page	4164
File Size	2968835
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transforms Hyperspectral imaging Correlation Detectors Principal component analysis Lighting Detection algorithms anomalous change detection hyperspectral
Content Type	Text
Resource Type	Article

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