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.
It is important to select representative patterns for successful classification of remotely sensing images. In the paper, a superpixel-based active learning (shorted as SPAL) algorithm is proposed to iteratively select informative unlabeled samples for labeling by experts. In particular, new representation of a remote sensing image is generated by grouping the data into perceptually meaningful atomic...
Derivatives of spectral reflectance signatures can capture salient features of different land-cover classes. Such information has been used for supervised classification of remote sensing data along with spectral reflectance. In the paper, we study how supervised classification of hyperspectral remote sensing data can benefit from the use of derivatives of spectral reflectance without the aid of other...
Semi-supervised learning using both labeled and unlabeled data is usually adopted to design a high-accuracy and robust classification system on small-size remote sensing training data set. As suggested in the machine learning literature, the larger amount of unlabeled patterns are used, the better classification accuracies can be obtained. Nevertheless, most recently proposed semi-supervised algorithms...
Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multi-spectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides,...
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.