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.
The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold coordinates. The performance of these algorithms is inextricably tied to the graphical model constructed from the spectral data, i.e., the community structure of the spectral data must be well represented to extract meaningful information. This paper provides a survey...
The Schroedinger Eigenmaps (SE) embedding has been previously introduced and applied to spectral target detection problems in hyperspectral imagery (HSI). The proposed SE-based detection approach combines the spectral and spatial connectivity of target-like pixels into the Schroedinger operator by using a “knowledge propagation” scheme. Likewise, it has been noted the impact that the local data structure...
Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform...
The analysis of remotely sensed spectral imagery has a variety of applications in both the public and private sectors, including tracking urban development, monitoring the spread of diseased crops, and mapping environmental disasters. The high spatial and spectral resolutions in hyperspectral imagery (HSI) make it particularly desirable for these types of analyses, as HSI sensors capture “color” information...
Hyperspectral image data are traditionally analyzed using statistical models. However, as the spatial and spectral resolutions of the images improve as a result of advances in sensor technology, the data no longer maintain a Gaussian distribution; this is due to increased material diversity in the scene, i.e., clutter. This causes many statistical assumptions about the data — and subsequently, the...
Spectral anomaly detection in hyperspectral imagery is concerned with identifying unusual spectra that occur infrequently within the image. The modelappliedinthis paper is based on the idea that background pixels reside on, or near, lower dimensional manifolds (surfaces) in the higher-dimensional spectral space. Using a similarity graph to approximate the theoretical background manifold(s), we investigate...
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.