DocumentCode :
961871
Title :
The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data
Author :
Nielsen, Allan Aasbjerg
Author_Institution :
Informatics & Math. Modelling, Tech. Univ. Denmark, Lyngby
Volume :
16
Issue :
2
fYear :
2007
Firstpage :
463
Lastpage :
478
Abstract :
This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted (IR) MAD method in a series of iterations places increasing focus on "difficult" observations, here observations whose change status over time is uncertain. The MAD method is based on the established technique of canonical correlation analysis: for the multivariate data acquired at two points in time and covering the same geographical region, we calculate the canonical variates and subtract them from each other. These orthogonal differences contain maximum information on joint change in all variables (spectral bands). The change detected in this fashion is invariant to separate linear (affine) transformations in the originally measured variables at the two points in time, such as 1) changes in gain and offset in the measuring device used to acquire the data, 2) data normalization or calibration schemes that are linear (affine) in the gray values of the original variables, or 3) orthogonal or other affine transformations, such as principal component (PC) or maximum autocorrelation factor (MAF) transformations. The IR-MAD method first calculates ordinary canonical and original MAD variates. In the following iterations we apply different weights to the observations, large weights being assigned to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights being assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original variables. To stabilize solutions to the (IR-)MAD problem, some form of regularization may be needed. This is especially useful for work on hyperspectral data. This paper describes or- - dinary two-set canonical correlation analysis, the MAD transformation, the iterative extension, and three regularization schemes. A simple case with real Landsat Thematic Mapper (TM) data at one point in time and (partly) constructed data at the other point in time that demonstrates the superiority of the iterative scheme over the original MAD method is shown. Also, examples with SPOT High Resolution Visible data from an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization
Keywords :
affine transforms; geophysical signal processing; iterative methods; object detection; principal component analysis; remote sensing; Kenya; Landsat Thematic Mapper; MAD method; affine transformations; agricultural region; calibration schemes; canonical correlation analysis; change detection; data normalization; hyperspectral airborne HyMap data; hyperspectral data; maximum autocorrelation factor; multispectral data; multivariate alteration detection method; principal component; regularized iteratively reweighted method; remote sensing imagery; separate linear transformations; southeastern Germany; Autocorrelation; Boosting; Calibration; Data mining; Gain measurement; Hyperspectral imaging; Hyperspectral sensors; Iterative methods; Remote sensing; Time measurement; Canonical correlation analysis (CCA); MAD transformation; iteratively reweighted multivariate alteration detection (IR-MAD); regularization or penalization; remote sensing; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Graphics; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Multivariate Analysis; Numerical Analysis, Computer-Assisted; Signal Processing, Computer-Assisted; Subtraction Technique; User-Computer Interface;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2006.888195
Filename :
4060945
Link To Document :
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