Subpixel Anomalous Change Detection in Remote Sensing Imagery

A machine-learning framework for anomalous change detection is extended to the situation in which the anomalous change is smaller than a pixel. Although the existing framework can be applied to (and does have power against) the subpixel case, it is possible to optimize that framework for the subpixel case when the size of the anomalous change is known. The limit of intesimally small anomaly turns out to be well- defined, and provides a new parameter-free anomalous change detector which is effective over a range of subpixel anomalies, and continues to have reasonable power against the full-pixel case.

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

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

[3]  Robert P. W. Duin,et al.  Uniform Object Generation for Optimizing One-class Classifiers , 2002, J. Mach. Learn. Res..

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

[5]  James Theiler,et al.  Resampling approach for anomalous change detection , 2007, SPIE Defense + Commercial Sensing.

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

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

[8]  Pramod K. Varshney,et al.  An image change detection algorithm based on Markov random field models , 2002, IEEE Trans. Geosci. Remote. Sens..

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

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

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

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