Results 271 to 280 of about 11,136 (312)

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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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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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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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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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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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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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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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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Atmospheric and topographic corrections for hyperspectral imagery

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
In mountainous areas, slope and altitude variations modulate the airborne sensed hyperspectral radiance image. A new algorithm, SIERRA, has been developed for atmospheric, relief and BRDF corrections in order to extract the surface reflectance in the form of bi-hemispherical albedo that does not depend on solar incidence and observation angles.
Véronique Achard, Xavier Lenot
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Adaptive Compressed Classification for hyperspectral imagery

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Hyperspectral imaging (HSI) is a useful tool for the classification of vast areas. High accuracy is achieved by means of spectral information for each pixel, which inherently leads to a huge amount of data and, thus, requires costly processing.
Jürgen T. Hahn, Simon Rosenkranz, Abdelhak M. Zoubir   +2 more
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