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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...
Compressive sensing (CS) takes advantage of the spatial and spectral redundancy in hyperspectral imagery to take fewer measurements than traditional sensors. We simulate compressively sensed hyperspectral airborne images of a HyMap image of Cooke City, Montana using the Coded Aperture Snapshot Spectral Imager Dual Disperser (CASSI-DD) sensor model. Flake et al's novel algorithm (2013), which incorporates...
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 this paper we present a new methodology for automated target detection and identification in hyperspectral imagery. The standard paradigm for target detection in hyperspectral imagery is to run a detection algorithm, typically statistical in nature, and visually inspect each high-scoring pixel to decide whether it is a true detection or a false alarm. Detection filters have constant false alarm...
Many techniques from graph theory and network theory have been applied to traditional images, and some techniques are now being applied to spectral imagery. Contrary to the typical approaches of utilizing the first order statistics, mixture models, and linear subspaces, the methods described in this paper utilize the spectral data structure to generate a graph representation of the image. By ignoring...
Accurate detection and clustering are two of the main analysis tasks for remotely sensed spectral imagery. Hyper-spectral image (HSI) analysis often involves mathematically transforming the raw data into a new space using Principal Components Analysis (PCA) or similar techniques where a lower dimensional subspace containing most of the image information may be extracted. The results of standard algorithms...
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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