N. Nasrabadi

Publications478
h-index48
Citations22,354
Highly Influential Citations1,319
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Pattern Recognition and Machine Learning
TLDR
Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
TLDR
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.
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Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery
  • H. Kwon, N. Nasrabadi
  • Computer Science
    IEEE Transactions on Geoscience and Remote…
  • 24 January 2005
TLDR
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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Sparse Representation for Target Detection in Hyperspectral Imagery
In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a…
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Hyperspectral Remote Sensing Data Analysis and Future Challenges
TLDR
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.
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Hyperspectral Image Classification via Kernel Sparse Representation
TLDR
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.
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Joint Sparse Representation for Robust Multimodal Biometrics Recognition
TLDR
A multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations, which simultaneously takes into account correlations as well as coupling information among biometric modalities.
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Multi-View Automatic Target Recognition using Joint Sparse Representation
We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the…
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Design of Non-Linear Kernel Dictionaries for Object Recognition
TLDR
It is shown that nonlinear dictionary learning approaches can provide significantly better performance compared with their linear counterparts and kernel principal component analysis, especially when the data is corrupted by different types of degradations.
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Image coding using vector quantization: a review
TLDR
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.
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