The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which...
We use singular vectors of the whitened cross-covariance matrix of two hyper-spectral images and the Golub-Kahan permutations in order to obtain equivalent tridiagonal representations of the coefficient matrices for a family of covariance-based quadratic Anomalous Change Detection (ACD) algorithms. Due to the nature of the problem these tridiagonal matrices have block-diagonal structure, which we...
We introduce a simple approach to compensate for the effects of residual misregistration on the performance of anomalous change detection algorithms. Using real data, both within a simulation framework for anomalous changes, and with a real anomalous change, we illustrate the approach and investigate its effectiveness.
The formalism of anomalous change detection, which was developed for finding unusual changes in pairs of images, is extended to sequences of more than two images. Extended algorithms based on RX, Chronochrome, and Hyper are presented for identifying the most anomalously changing pixels in a sequence of co-registered images. Experimental comparisons are performed both on real data with real anomalies...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.