Results 281 to 290 of about 45,417 (317)

Georegistration of airborne hyperspectral image data

IEEE Transactions on Geoscience and Remote Sensing, 2001
A suite of geometric sensor and platform modeling tools has been developed which have achieved consistent subpixel accuracy in orthorectification experiments. Aircraft platforms in turbulent atmospheric conditions present unique challenges and have required creative modeling approaches.
C. Lee, James Bethel
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Hyperspectral Data Exploitation

2016
The main objective of hyperspectral imaging remote sensing is the identification of materials or phenomena from their reflectance or emissivity spectra to serve the needs of different applications. In this chapter, building on the understanding of the phenomenology of spectral remote sensing and the introduced signal processing methods, we develop ...
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Linear Data Compression of Hyperspectral Images

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
The aim of the paper is to analyse hyperspectral images using tensor principal component analysis of multi-way data sets. The mathematical and computational backgrounds of pattern recognition are the geometries in Hilbert space for functional analysis and applied linear algebra for numerical analysis, respectively.
Kaori Tanji, Atsushi Imiya, Hayato Itoh, Hiroaki Kuze, Naohiro Manago   +4 more
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Sparse Representations for Hyperspectral Data Classification

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
We investigate the use of sparse principal components for representing hyperspectral imagery when performing feature selection. For conventional multispectral data with low dimensionality, dimension reduction can be achieved by using traditional feature selection techniques for producing a subset of features that provide the highest class separability,
Salman Siddiqui, Stefan A. Robila, Jing Peng 0001, Dajin Wang   +3 more
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Application of Hyperspectral Data

2016
Three decades of airborne imaging spectroscopy have demonstrated the added value of this remote sensing technique to improve the understanding of Earth’s functioning. With the advent of airborne imaging spectroscopy, the specialized image processing system has made the generation of quantitative methods such as semi-analytical and analytical methods ...
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Partial generalized correlation for hyperspectral data

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2011
A variational approach is proposed for the unsupervised assessment of attribute variability of high-dimensional data given a differentiable similarity measure. The key question addressed is how much each data attribute contributes to an optimum transformation of vectors for reaching maximum similarity.
Marc Strickert, Björn Labitzke, Volker Blanz   +2 more
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CDC-MRF for Hyperspectral Data Classification

2018
This paper presents a new hyperspectral classification algorithm based on convolutional neural network (CNN). A CNN is first used to learn the posterior class distributions using a patch-wise training strategy to better utilize the spatial information.
Yuanyuan Li, Jingjing Zhang, Chun-Hou Zheng 0001, Qing Yan, Li-Na Xun   +4 more
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Interest Points for Hyperspectral Image Data

IEEE Transactions on Geoscience and Remote Sensing, 2009
Interest points are widely used as point-features for image matching. This paper describes robust and efficient algorithms to extract multiscale interest points in hyperspectral images in which structural information is distributed across several spectral bands. The formulation is based on a Gaussian scale-space representation of the hyperspectral data
Amit Mukherjee, Miguel Velez-Reyes, Badrinath Roysam   +2 more
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Hyperspectral Data Compression

2006
Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery.Chapter 1 addresses compression architecture, and reviews and compares compression methods.
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On the reliability of PCA for complex hyperspectral data

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
Principal Component Analysis (PCA) is a popular tool for initial investigation of hyperspectral image data. There are many ways in which the estimated eigenvalues and eigenvectors of the covariance matrix are used. Further steps in the analysis or model building for hyperspectral images are often dependent on those estimated quantities. It is therefore
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