Category: Conference Papers

Abstract: The effectiveness of non-negative matrix factorization (NMF) depends on a suitable choice of the number of bases, which is often difficult to decide in practice. This paper imposes sparseness on the factorization coefficients in order to determine the number of bases automatically during the decomposition process. The benefit of sparse promotion for NMF is […]

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: 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: 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: Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor. The aim of this work is to develop a hierarchical Bayesian framework for estimating sand ripple characteristics from SAS imagery that can make use of multiple passes over an area at a variety of ranges. Using a hierarchical Bayesian framework […]

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: In this paper we describe an unsupervised approach to seabed co-segmentation over the multiple sonar images collected in sonar surveys. We adapt a traditional single image segmentation texton-based approach to the sonar survey task by modifying the texture extraction filter bank to better model possible sonar image textures. Two different algorithms for building a […]

Abstract: A possibilistic K-Nearest Neighbors classifier is presented to classify mine and non-mine objects using data collected from a wideband electromagnetic induction (WEMI) sensor. The proposed classifier is motivated by the observation that buried objects often have consistent signatures depending on their metal content, size, shape, and depth. Given a joint orthogonal matching pursuits (JOMP) […]

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. […]