N
Nasser M. Nasrabadi
Researcher at West Virginia University
Publications - 531
Citations - 15531
Nasser M. Nasrabadi is an academic researcher from West Virginia University. The author has contributed to research in topics: Vector quantization & Artificial neural network. The author has an hindex of 51, co-authored 511 publications receiving 13203 citations. Previous affiliations of Nasser M. Nasrabadi include United States Army Research Laboratory & University at Buffalo.
Papers
More filters
Journal ArticleDOI
Hyperspectral Remote Sensing Data Analysis and Future Challenges
Jose M. Bioucas-Dias,Antonio Plaza,Gustau Camps-Valls,Paul Scheunders,Nasser M. Nasrabadi,Jocelyn Chanussot +5 more
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Journal ArticleDOI
Image coding using vector quantization: a review
Nasser M. Nasrabadi,R.A. King +1 more
TL;DR: First, the concept of vector quantization is introduced, then its application to digital images is explained, and the emphasis is on the usefulness of the vector quantification when it is combined with conventional image coding techniques, orWhen it is used in different domains.
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.
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.
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.