Simulation framework for spatio-spectral anomalous change detection

We describe the development of a simulation framework for anomalous change detection that considers both the spatial and spectral aspects of the imagery. A purely spectral framework has previously been introduced, but the extension to spatio-spectral requires attention to a variety of new issues, and requires more careful modeling of the anomalous changes. Using this extended framework, we evaluate the utility of spatial image processing operators to enhance change detection sensitivity in (simulated) remote sensing imagery.

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

[2]  S. Macenka,et al.  Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1988 .

[3]  Alan A. Stocker,et al.  Advanced algorithms for autonomous hyperspectral change detection , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[4]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

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

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

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

[8]  S. Rotman,et al.  Spatial-spectral filtering for the detection of point targets in multi- and hyperspectral data , 2005 .

[9]  James Theiler,et al.  A structural framework for anomalous change detection and characterization , 2009, Defense + Commercial Sensing.

[10]  Russell C. Hardie,et al.  Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations , 2008, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

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

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