Multi- and hyperspectral remote sensing change detection with generalized difference images by the IR-MAD method

Abstract

Change detection methods for multi- and hyper- variate data aim at identifying differences in data acquired over the same area at different points in time. In this con- tribution an iterative extension to the multivariate alteration detection (MAD) transformation for change detection is sketched and applied. The MAD transformation is based on canonical correlation analysis (CCA), which is an established technique in multivariate statistics. The extension in an iterative scheme seeks to establish an increasingly better background of no-change against which to detect change. This is done by putting higher weights on observations of no-change in the calculation of the statistics for the CCA. The differences found may be due to noise or differences in (atmospheric etc.) conditions at the two acquisition time points. To prevent a change detection method from detecting uninteresting change due to noise or arbitrary spurious differences the application of regularization, also known as penalization, and other types of robustification of the change detection method may be important especially when applied to hyperspectral data. Among other things results show that the new iterated scheme does give a better no-change background against which to detect change than the original, non-iterative MAD method and that the IR-MAD method depicts the change detected in less noisy components. I. INTRODUCTION This contribution focuses on construction of more gen- eral difference images than simple differences in multivariate change detection. This is done via an iterated version (1) of the canonical correlation analysis (CCA) (2) based multivariate alteration detection (MAD) method (3) that could, moreover, be combined with an expectation-maximization (EM) based method for determining thresholds for differentiating between change and no-change in the difference images, and for estimating the variance-covariance structure of the no-change observations (4), (5). The variances can be used to estab- lish a single change/no-change image based on the general multivariate difference image. The resulting imagery from MAD based change detection is invariant to linear and affine transformations of the input including, e.g., affine corrections to normalize data between the two acquisition time points. This is an enormous advantage over other multivariate change detection methods. The resulting single change/no-change image can be used to establish both change regions and to extract observations with which a fully automated orthogonal regression analysis based normalization of the multivariate data between the two points in time can be developed (6). Results (not shown here) from partly simulated multivariate data indicate an improved performance of the iterated scheme over the original MAD method (1). Also, a few comparisons with established methods for calculation of robust statistics for the CCA indicate that the scheme suggested here performs better, see also (7). Regularization issues typically important in connection with the analysis of hyperspectral data are dealt with in (8)-(10) and briefly mentioned here.