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Two of the biggest challenges in analyzing HyperSpectral Image (HSI) data are that, first, the data is very high-dimensional, and secondly, by its very nature, HSI contains both spatial and spectral information. In order to make full use of this information, models and algorithms should incorporate both aspects of the data; unfortunately, this is a decidedly non-trivial problem. In recent years, spectral...
Widely used methods of spectral clustering, target, and anomaly detection when applied to spectral imagery provide less than desirable results across sensor type, scene content, spectral and spatial resolutions due to the complex nature of the data. This results in a large burden placed on the analyst in terms of the amount of data needed to be processed and the ability to discern the difference between...
We propose a novel unsupervised segmentation method that efficiently exploits spectral intensity, gradient and textural information in remotely sensed imagery. Our approach begins in a multi-band gradient detection scheme of the input scene, utilized to determine the spectral intensity variations across it. The obtained gradient map is employed in an iterative region growth procedure that originates...
In recent years, many new methods for analyzing spectral imagery have been introduced. These new methods have been developed to improve the analysis of hyperspectral imagery. Many of these techniques are data driven anomaly/target detection and spectral clustering algorithms which are used to decide whether a particular pixel or area is “interesting.” For this research, a group of these algorithms...
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