Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Detecting Changes in Aerial Views of Man-Made Structures
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Change detection in sequences of images by multifractal analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
IEEE Transactions on Image Processing
Semi-parametric optimization for missing data imputation
Applied Intelligence
Video synchronization as one-class learning
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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Identifying interesting changes from a sequence of overhead imagery—as opposed to clutter, lighting/seasonal changes, etc.—has been a problem for some time. Recent advances in data mining have greatly increased the size of datasets that can be attacked with pattern discovery methods. This paper presents a technique for using predictive modeling to identify unusual changes in images. Neural networks are trained to predict “before” and “after” pixel values for a sequence of images. These networks are then used to predict expected values for the same images used in training. Substantial differences between the expected and actual values represent an unusual change. Results are presented on both multispectral and panchromatic imagery.