Abstract: The functions of multiple instances (FUMI) approach for learning target and nontarget signatures is introduced. FUMI is a generalization of the multiple-instance learning (MIL) approach for supervised learning. FUMI differs significantly from standard MIL and supervised learning approaches because… Read More
Journal PapersJournal Papers
Spatial and spectral unmixing using the beta compositional model
Abstract: This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the proportion of each endmember at every pixel of a hyperspectral image. Under the BCM, each endmember… Read More
Earth movers distance-based simultaneous comparison of hyperspectral endmembers and proportions
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… Read More
Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing
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… Read More
Comparing fuzzy, probabilistic, and possibilistic partitions using the earth mover’s distance
Abstract: A number of noteworthy techniques have been put forth recently in different research fields for comparing clusterings. Herein, we introduce a new method for comparing soft (fuzzy, probabilistic, and possibilistic) partitions based on the earth mover’s distance (EMD) and… Read More
Piecewise convex multiple-model endmember detection and spectral unmixing
Abstract: A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of… Read More
Sampling piecewise convex unmixing and endmember extraction
Abstract: A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore,… Read More
Editorial: Algorithms for multispectral and hyperspectral image analysis
Abstract: Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities in remote sensing—higher spatial and/or spectral resolution over larger areas leads to more detailed and comprehensive land cover mapping… Read More
Spectral unmixing cluster validity index for multiple sets of endmembers
Abstract: A hyperspectral pixel is generally composed of a relatively small number of endmembers. Several unmixing methods have been developed to enforce this concept through sparsity promotion or piece-wise convex mixing models. Piece-wise convex unmixing methods often require as parameters… Read More
Directly measuring material proportions using hyperspectral compressive sensing
Abstract: A compressive sensing framework is described for hyperspectral imaging. It is based on the widely used linear mixing model, LMM, which represents hyperspectral pixels as convex combinations of small numbers of endmember (material) spectra. The coefficients of the endmembers… Read More