Resampling approach for anomalous change detection

We investigate the problem of identifying pixels in pairs of co-registered images that correspond to real changes on the ground. Changes that are due to environmental differences (illumination, atmospheric distortion, etc.) or sensor differences (focus, contrast, etc.) will be widespread throughout the image, and the aim is to avoid these changes in favor of changes that occur in only one or a few pixels. Formal outlier detection schemes (such as the one-class support vector machine) can identify rare occurrences, but will be confounded by pixels that are "equally rare" in both images: they may be anomalous, but they are not changes. We describe a resampling scheme we have developed that formally addresses both of these issues, and reduces the problem to a binary classification, a problem for which a large variety of machine learning tools have been developed. In principle, the effects of misregistration will manifest themselves as pervasive changes, and our method will be robust against them - but in practice, misregistration remains a serious issue.

[1]  N. Nasrabadi,et al.  Kernel RX : A new nonlinear anomaly detector , 2005 .

[2]  Maya Gokhale,et al.  Detecting a malicious executable without prior knowledge of its patterns , 2005, SPIE Defense + Commercial Sensing.

[3]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[4]  Alan P. Schaum,et al.  Subclutter target detection using sequences of thermal infrared multispectral imagery , 1997, Defense, Security, and Sensing.

[5]  Andrew W. Moore,et al.  Active Learning for Anomaly and Rare-Category Detection , 2004, NIPS.

[6]  Dimitris G. Manolakis,et al.  Using elliptically contoured distributions to model hyperspectral imaging data and generate statistically similar synthetic data , 2004, SPIE Defense + Commercial Sensing.

[7]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[8]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[9]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[10]  James Theiler,et al.  Resampling approach for anomaly detection in multispectral images , 2003, SPIE Defense + Commercial Sensing.

[11]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[12]  Dimitris G. Manolakis,et al.  Modeling hyperspectral imaging data , 2003, SPIE Defense + Commercial Sensing.

[13]  Farid Melgani,et al.  Markovian Fusion Approach to Robust Unsupervised Change Detection in Remotely Sensed Imagery , 2006, IEEE Geoscience and Remote Sensing Letters.

[14]  Chris Clifton.  Change Detection in Overhead Imagery Using Neural Networks , 2004, Applied Intelligence.

[15]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[16]  Don R. Hush,et al.  Density Level Detection is Classification , 2004, NIPS.

[17]  Neal R. Harvey,et al.  Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Shai Ben-David,et al.  Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher , 1997, J. Comput. Syst. Sci..

[20]  Lakshman Prasad,et al.  Vectorized Image Segmentation via Trixel Agglomeration , 2005, GbRPR.

[21]  John P. Kerekes,et al.  Statistics of hyperspectral imaging data , 2001, SPIE Defense + Commercial Sensing.

[22]  Alan P. Schaum,et al.  Hyperspectral change detection and supervised matched filtering based on covariance equalization , 2004, SPIE Defense + Commercial Sensing.

[23]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[24]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[25]  James Theiler,et al.  Proposed Framework for Anomalous Change Detection , 2006 .