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.
Using spectra of hyperspectral remote sensing imagery to identify and classify land cover has been a hot topic thanks for its high resolution spectrum. However, when the quantity of labeled samples is too small, the classification accuracy of hyperspectral data will be reduced greatly. Most classification algorithms take dimensional reduction strategy and require plentiful labeled samples in order...
Statistic classification of hyperspectral data is a great challenge because of its large number of spectral channels, especially when the labeled training samples are relatively few. Most of the classification methods require using a large number of training samples, but in remote sensing situations, identifying and labeling samples are extremely difficult and expensive. A sparse representation classification...
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.