Local Coregistration Adjustment for Anomalous Change Detection

We describe an approach for improving the robustness to misregistration of pixel-wise anomalous change detection (ACD) algorithms. The aim of ACD is to distinguish actual anomalous changes from the irrelevant incidental differences that occur throughout the scene. For such change detection to be effective, it is important that corresponding pixels in the two images of interest correspond to the same location in the scene. Indeed, one of the most confounding sources of incidental differences is the inevitable imprecision in the coregistration of the two images. We address this with small local adjustments to the coregistration which leads to a modified misregistration-insensitive measure of anomalousness. Several variants are considered, and the resulting performance improvements are evaluated using both real and simulated changes, and real and simulated misregistration.

[1]  David A. Clausi,et al.  ARRSI: Automatic Registration of Remote-Sensing Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jordi Inglada,et al.  On the possibility of automatic multisensor image registration , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Neal R. Harvey,et al.  Simulation framework for spatio-spectral anomalous change detection , 2009, Defense + Commercial Sensing.

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

[5]  Marios S. Pattichis,et al.  Robust Multispectral Image Registration Using Mutual-Information Models , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Joseph Meola,et al.  Airborne hyperspectral detection of small changes. , 2008, Applied optics.

[7]  Michael T. Eismann,et al.  Image misregistration effects on hyperspectral change detection , 2008, SPIE Defense + Commercial Sensing.

[8]  Pramod K. Varshney,et al.  Performance of mutual information similarity measure for registration of multitemporal remote sensing images , 2003, IEEE Trans. Geosci. Remote. Sens..

[9]  Luís Corte-Real,et al.  Automatic Image Registration Through Image Segmentation and SIFT , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[10]  J. Theiler,et al.  Subpixel Anomalous Change Detection in Remote Sensing Imagery , 2008, 2008 IEEE Southwest Symposium on Image Analysis and Interpretation.

[11]  James Theiler,et al.  Quantitative comparison of quadratic covariance-based anomalous change detectors. , 2008, Applied optics.

[12]  Jordi Inglada,et al.  Change Detection with Misregistration Errors , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Pierre Soille,et al.  Change Detection Based on Information Measure , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[14]  A. Schaum,et al.  Linear chromodynamics models for hyperspectral target detection , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[15]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[16]  James Theiler,et al.  Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery , 2010, IEEE Geoscience and Remote Sensing Letters.

[17]  John P. Kerekes,et al.  Development of a Web-Based Application to Evaluate Target Finding Algorithms , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Jordi Inglada,et al.  Analysis of Artifacts in Subpixel Remote Sensing Image Registration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[19]  James Theiler,et al.  Improved change detection with local co-registration adjustments , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[20]  James Theiler,et al.  Sensitivity of anomalous change detection to small misregistration errors , 2008, SPIE Defense + Commercial Sensing.

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

[22]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[23]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[24]  James Theiler,et al.  Symmetrized local co-registration optimization for anomalous change detection , 2010, Electronic Imaging.