Results 161 to 170 of about 345,137 (201)

Lossless Compression of Hyperspectral Imagery

2011 First International Conference on Data Compression, Communications and Processing, 2011
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances ...
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Fast Band Selection for Hyperspectral Imagery

2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011
Band selection is a common technique for dimensionality reduction of hyperspectral imagery. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. However, it may be time-consuming for unsupervised band selection methods that need to take all pixels into ...
He Yang, Qian Du 0001
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Robust Sparse Unmixing for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2018
A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library.
Dan Wang 0005, Zhenwei Shi 0001, Xinrui Cui   +2 more
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Learning Sparse Codes for Hyperspectral Imagery

IEEE Journal of Selected Topics in Signal Processing, 2011
The spectral features in hyperspectral imagery (HSI) contain significant structure that, if properly characterized, could enable more efficient data acquisition and improved data analysis. Because most pixels contain reflectances of just a few materials, we propose that a sparse coding model is well-matched to HSI data.
Adam S. Charles, Bruno A. Olshausen, Christopher J. Rozell   +2 more
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Exploiting manifold geometry in hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2005
A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed and compared against the de facto standard of linear mixing. This new approach seeks a manifold coordinate system that preserves geodesic distances in the high-dimensional hyperspectral data space. Algorithms for deriving manifold coordinates, such as isometric
Charles M. Bachmann, Thomas L. Ainsworth, Robert A. Fusina   +2 more
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Hyperspectral Imagery Clustering With Neighborhood Constraints

IEEE Geoscience and Remote Sensing Letters, 2013
This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area.
Shanshan Li 0003, Bing Zhang 0001, An Li, Xiuping Jia, Lianru Gao, Man Peng   +5 more
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Sparsity-based classification of hyperspectral imagery

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
In this paper, a new sparsity-based classification algorithm for hyperspectral imagery is proposed. This algorithm is based on the concept that a pixel in hyperspectral imagery lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples.
Yi Chen 0014, Nasser M. Nasrabadi, Trac D. Tran   +2 more
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Color Representation and Classification for Hyperspectral Imagery

2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
A hyperspectral image contains information in hundreds of spectral channels. The information is sparsely distributed in such a huge 3D image cube. In practical applications, it may be helpful if such a high dimensional data can be displayed into an informative color image.
Qian Du 0001, Nareenart Raksuntorn, Shangshu Cai, Nicolas H. Younan   +3 more
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Anomaly discrimination and classification for hyperspectral imagery

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it generally cannot discriminate its detected anomalies one from another. One common approach is to measure closeness of spectral characteristics among detected anomalies to determine if the detected anomalies are actually ...
Li-Chien Lee, Drew Paylor, Chein-I Chang
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Unified mixing model for hyperspectral imagery

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
Multiple mixing models for hyperspectral imagery have been developed over the years. The most common is the linear mixing model which states that each pixel's spectral signature is a linear combination of the unique materials (or endmembers) in the scene.
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