Paul Gader - Academia.edu (original) (raw)

Papers by Paul Gader

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013

PeerJ, 2019

Tree species classification using hyperspectral imagery is a challenging task due to the high spe... more Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminative target signature to differentiate between a pair of tree species while accounting for label uncertainty. Multi-class species classification is achieved by training a set of one-vs-one MI-ACE classifiers corresponding to the classification between each pair of tree species and a majority voting on the classification results from all classifiers. Additionally, the performance of MI-ACE does not rely on parameter settings that require tuning resulting in a method that is easy to use in application. Results presented are using training and testing data provided by a data analysis competition aimed at encouraging the development of methods for extracting ecological information through re...

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, Jan 18, 2016

The normal compositional model (NCM) has been extensively used in hyperspectral unmixing. However... more The normal compositional model (NCM) has been extensively used in hyperspectral unmixing. However, previous research has mostly focused on estimation of endmembers and/or their variability, based on the assumption that the pixels are independent random variables. In this paper, we show that this assumption does not hold if all the pixels are generated by a fixed endmember set. This introduces another concept, endmember uncertainty, which is related to whether the pixels fit into the endmember simplex. To further develop this idea, we derive the NCM from the ground up without the pixel independence assumption, along with (i) using different noise levels at different wavelengths and (ii) using a spatial and sparsity promoting prior for the abundances. The resulting new formulation is called the spatial compositional model (SCM) to better differentiate it from the NCM. The SCM maximum a posteriori (MAP) objective leads to an optimization problem featuring noise weighted least-squares m...

In the linear mixing model, many techniques for endmember extraction are based on the assumption ... more In the linear mixing model, many techniques for endmember extraction are based on the assumption that pure pixels exist in the data, and form the extremes of a simplex embedded in the data cloud. These endmembers can then be obtained by geometrical approaches, such as looking for the largest sim-plex, or by maximal orthogonal subspace projections. Also obtaining the abundances of each pixel with respect to these endmembers can be completely written in geometrical terms. While these geometrical algorithms assume Euclidean geom-etry, it has been shown that using different metrics can offer certain benefits, such as dealing with nonlinear mixing effects by using geodesic or kernel distances, or dealing with correla-tions and colored noise by using Mahalanobis metrics. In this paper, we demonstrate how a linear unmixing chain based on maximal orthogonal subspace projections and simplex pro-jection can be written in terms of distance geometry, so that other metrics can be easily employed...

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015

Context-based unmixing has been studied by several re-searchers. Recent techniques, such as piece... more Context-based unmixing has been studied by several re-searchers. Recent techniques, such as piece-wise convex unmixing using fuzzy and possibilistic clustering or Bayesian methods proposed in [11] attempt to form contexts via clus-tering. It is assumed that the linear mixing model applies to each cluster (context) and endmembers and abundances are found for each cluster. As the clusters are spatially coher-ent, hyperspectral image segmentation can significantly aid unmixing approaches that perform cluster specific estimation of endmembers. In this work, we integrate a graph-cuts seg-mentation algorithm with piece-wise convex unmixing. This is compared to fuzzy clustering (FCM) with results obtained on two datasets. The results demonstrate that the integrated approach achieves better segmentation and more precise end-member identification (in terms of comparisons with known ground truth).