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.
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g. band specific noise, vertical or horizontal artifacts or saturated pixels...
This paper proposes a target detector based on kernel sparse and spatial constraint for hyperspectral imagery (HSI). Due to the nonlinear and structural features of HSI data, sparse representation and spatial constraint are taken into consideration. Firstly, we construct a dictionary to represent the target pixels within a small neighborhood by a linear combination of samples. Then, these targets...
Hyperspectral anomaly detection is playing an important role in remote sensing field. Most conventional detectors based on the Reed-Xiaoli (RX) method assume the background signature obeys a Gaussian distribution. However, it is definitely hard to be satisfied in practice. Moreover, background statistics is susceptible to contamination of anomalies in the processing windows, which may lead to many...
Anomaly is generally defined as an object that strays away from the background clutter. As for hyperspectral anomaly detection, most of the previous methods fail to fully take advantage of the knowledge in both spatial and spectral domain. In this paper, we propose a novel method based on tensor recovery in which spatial structures and spectral characters are reasonably considered to separate the...
We propose sparse representation-based archetypal graphs as input to spectral clustering for anomaly and change detection. The graph consists of vertices defined by data samples and edges which weights are determines by sparse representation. Besides relationships between all data samples, the graph also encodes the relationship to extremal points, so-called archetypes, which leads to an easily interpretable...
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.