T
Trac D. Tran
Researcher at Johns Hopkins University
Publications - 355
Citations - 11135
Trac D. Tran is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Sparse approximation & Compressed sensing. The author has an hindex of 46, co-authored 351 publications receiving 9828 citations. Previous affiliations of Trac D. Tran include United States Naval Research Laboratory & Fraunhofer Society.
Papers
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Journal ArticleDOI
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
TL;DR: Experimental results show that the proposed sparsity-based algorithm for the classification of hyperspectral imagery outperforms the classical supervised classifier support vector machines in most cases.
Proceedings ArticleDOI
Sparsity adaptive matching pursuit algorithm for practical compressed sensing
TL;DR: This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing, called the sparsity adaptive matching pursuit, which provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases.
Journal ArticleDOI
Hyperspectral Image Classification via Kernel Sparse Representation
TL;DR: Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.
Journal ArticleDOI
Sparse Representation for Target Detection in Hyperspectral Imagery
TL;DR: This paper proposes a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI) based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples.
Journal ArticleDOI
Fast and Efficient Compressive Sensing Using Structurally Random Matrices
TL;DR: Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed sensing framework, called Structurally Random Matrix (SRM), which has theoretical sensing performance comparable to that of completely random sensing matrices.