H
Heesung Kwon
Researcher at United States Army Research Laboratory
Publications - 146
Citations - 3776
Heesung Kwon is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Object detection & Kernel embedding of distributions. The author has an hindex of 23, co-authored 134 publications receiving 2746 citations. Previous affiliations of Heesung Kwon include University at Buffalo & University of Massachusetts Amherst.
Papers
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Journal ArticleDOI
Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery
Heesung Kwon,Nasser M. Nasrabadi +1 more
TL;DR: It is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX- algorithm in the feature space in terms of kernels that implicitly compute dot products in thefeature space.
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Going Deeper With Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee,Heesung Kwon +1 more
TL;DR: A novel deep convolutional neural network that is deeper and wider than other existing deep networks for hyperspectral image classification, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors.
Journal ArticleDOI
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee,Heesung Kwon +1 more
TL;DR: In this article, a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification is proposed, which can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors.
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Adaptive anomaly detection using subspace separation for hyperspectral imagery
TL;DR: Adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials are proposed and the detection performance for each method is evaluated.
Journal ArticleDOI
Kernel matched subspace detectors for hyperspectral target detection
Heesung Kwon,Nasser M. Nasrabadi +1 more
TL;DR: A kernel realization of a matched subspace detector that is based on a subspace mixture model defined in a high-dimensional feature space associated with a kernel function, which showed superior detection performance over the conventional MSD when tested on several synthetic data and real hyperspectral imagery.