Results 271 to 280 of about 45,417 (317)

Simulation of the hyperspectral data using Multispectral data

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Hyperspectral remote sensing has been a research focus in recent years for various landcover applications. Hyperspectral Remote Sensing (HRS) sensors are majorly utilized in detailed Land Use Landover (LULC) studies which are not plausible using Multispectral Remote Sensing (MRS) sensors.
Varun Tiwari, Vinay Kumar, Kamal Pandey, Rigved Ranade, Shefali Agrawal   +4 more
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Processing of hyperspectral data

2023
Hyperspectral imagery is becoming increasingly important in remote sensing. Traditional airborne or ground-based hyperspectral imagery is more often complemented by satellite imagery. The technology for production and miniaturisation of hyperspectral sensors has advanced to the point where relatively high quality imagery can be acquired from unmanned ...
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Random forest classifiers for hyperspectral data

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
Two random forest (RF) approaches are explored; the RF-BHC (binary hierarchical classifier) and the RF-CART (classification and regression tree). Both methods are based on a collection (forest) of tree-like classifier systems where the difference is in the way the trees are grown.
Sveinn R. Joelsson, Jón Atli Benediktsson, Johannes R. Sveinsson   +2 more
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Hyperspectral data analysis by mixed transforms

IEEE International Geoscience and Remote Sensing Symposium, 2003
Aim of this paper is investigating the use of overcomplete bases for the representation of hyperspectral image data. The idea is building an overcomplete basis starting from several orthogonal or nonorthogonal bases and picking a set of vectors fitting pixel spectra to the largest extent.
ALPARONE, LUCIANO, ARGENTI, FABRIZIO, DIONISIO, MICHELE   +2 more
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European perspectives in hyperspectral data analysis

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
This paper explains some of the the goals and objectives of the newly started HYPER-I-NET Marie Curie Research and Training Network. In particular, the requirements related to the definition and implementation of an efficient, adequate and sufficiently general data processing chain for hyperspectral data analysis are considered.
Paolo Gamba, Antonio J. Plaza, Jón Atli Benediktsson, Jocelyn Chanussot   +3 more
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Spectral quality indicators for hyperspectral data

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
A novel approach to estimating at-sensor hyperspectral (HRS) data quality Q/A of Q/I is proposed. As the HRS sensor's performance may vary in time and space, a method to assess at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and ...
Anna Brook, Eyal Ben-Dor
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Collaborative sparse unmixing of hyperspectral data

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori.
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
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Parallel sparse unmixing of hyperspectral data

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction ...
José M. Rodriguez Alves, José M. P. Nascimento, José M. Bioucas-Dias, Antonio Plaza, Vítor Silva 0001   +4 more
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Robust sparse unmixing of hyperspectral data

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Sparse unmixing (SU) of hyperspectral data has recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (
Yong Ma 0001, Chang Li 0001, Jiayi Ma 0001   +2 more
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The inpainting of hyperspectral images: a survey and adaptation to hyperspectral data

SPIE Proceedings, 2012
In this work, we survey image reconstruction methods for hyperspectral imagery. First, a review of image interpolation methods, both linear and nonlinear, is given. Second, image inpainting methods, especially from the variational perspective, are analyzed with respect to their suitability for hyperspectral inpainting.
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