Tag: unmixing

Abstract: Context-based unmixing has been studied by several researchers. Recent techniques, such as piece-wise convex unmixing using fuzzy and possibilistic clustering or Bayesian methods proposed in [11] attempt to form contexts via clustering. It is assumed that the linear mixing model applies to each cluster (context) and endmembers and abundances are found for each cluster. […]

Abstract: A new approach for simultaneously comparing sets of hyperspectral endmembers and proportion values using the Earth Movers Distance (EMD) is presented. First, the EMD is defined and calculated per-pixel based on the proportion values and corresponding endmembers. Next, these per-pixel EMD distances are aggregated to obtain a final measure of dissimilarity. In particular, the […]

Abstract: An extension of the Function of Multiple Instances (FUMI) algorithm for target characterization is presented. FUMI is a generalization of Multiple Instance Learning (MIL). However, FUMI differs significantly from standard MIL and supervised learning approaches because only data points which are functions of class concepts are available. For applicability to hyperspectral data, this paper […]

Abstract: Imaging spectrometers measure the response from materials across the electromagnetic spectrum. Often, in remote sensing applications, the imaging spectrometers have low spectral resolution resulting in most measurements being mixed spectra from a scene. In these cases, pixels are assumed to be mixtures of pure spectra known as endmembers. Given the prevalence of mixed spectra, […]

Abstract: Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been […]

Abstract: Hyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across hundreds of spectral bands (Goetz, et al., 1985). The hyperspectral image yielded by such system can be represented by […]

Abstract: Much work in the study of hyperspectral imagery has focused on macroscopic mixtures and unmixing via the linear mixing model. A substantially different approach seeks to model hyperspectral data non-linearly in order to accurately describe intimate or microscopic relationships of materials within the image. In this paper we present and discuss a new model […]

Abstract: A new algorithm for subpixel target detection in hyperspectral imagery is proposed which uses the PFCM-FLICM-PCE algorithm to model and estimate the parameters of the image background. This method uses the piece-wise convex mixing model with spatial-spectral constraints, and uses possibilistic and fuzzy clustering techniques to find the piece-wise convex regions and robustly estimate […]

Abstract: In this paper, the SimUltaneous Band-weighting and Spectral Unmixing for Multiple Endmember Sets (SUBSUME) which performs endmember extraction for multiple sets of endmembers, estimates proportion values, and assigns partition-specific band weights is presented. By incorporating simultaneous band weighting, input hyperspectral data is partitioned while focusing on spectral information from the wavelengths that provide the […]

Abstract: This paper introduces a beta compositional model as a mixing model for hyperspectral images. Endmembers are represented via beta distributions, hereafter referred to as betas, to constrain endmembers to a physically-meaningful range. Two associated spectral unmixing algorithms are described and applied to simulated and real hyperspectral imagery. Links: Citation: A. Zare, P. Gader, D. […]