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.
Multi-sharpening consists in fusing a multispectral image with a hyperspectral one, to produce an unobservable image with the high spatial resolution of the former and the high spectral resolution of the latter. In this paper, a new fusion method, based on the spectral unmixing concept, is proposed. The proposed method, related to linear-quadratic spectral unmixing techniques, and based on linear-quadratic...
Hypersharpening aims at combining an observable low-spatial resolution hyperspectral image with a high-spatial resolution remote sensing image, in particular a multispectral one, to generate an unobservable image with the high spectral resolution of the former and the high spatial resolution of the latter. In this paper, two such new fusion methods are proposed. These methods, related to linear spectral...
In this paper, a new projected-gradient method for linear-quadratic matrix factorization is proposed for extracting hyperspectral endmember spectra. The proposed method is designed for a linear-quadratic mixing model involved in urban hyperspectral remote sensing images. The reduction of the number of considered variables, when optimizing the used cost function, constitutes the main originality of...
In this paper, a new projected-gradient method for bilinear matrix factorization with nonnegativity constraints is proposed for extracting hyperspectral endmember spectra. The proposed method is designed for a bilinear mixing model faced in urban hyperspectral remote sensing images. Experiments based on realistic synthetic data, generated according to the considered bilinear mixing model, are conducted...
In this paper, we propose a new multi-sharpening approach for improving the spatial resolution of hyperspectral data. This approach, based on the linear-quadratic spectral unmixing concept, uses a linear-quadratic nonnegative matrix factorization multiplicative algorithm. Our method first consists in unmixing the low spatial resolution hyperspectral data and high spatial resolution multispectral data...
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.