Results 171 to 180 of about 345,137 (201)

Mixed Noise Reduction in Hyperspectral Imagery

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019
In this paper, a hyperspectral mixed noise reduction technique is proposed called HyMiR. We assume that hyperspectral images include Gaussian and sparse noise types. First, the Gaussian noise is removed using a recently-developed method called hyperspectral restoration (HyRes).
Behnood Rasti, Pedram Ghamisi, Jocelyn Chanussot   +2 more
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Constrained band selection for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2006
Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (
Chein-I Chang, Su Wang 0002
openaire   +2 more sources

Toward the Vectorization of Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2023
Leyuan Fang, Yinglong Yan, Jun Yue 0004, Yue Deng 0001   +3 more
openaire   +1 more source

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
openaire   +1 more source

Nonlinear mixture analysis for hyperspectral imagery

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another “endmember”, representing nonlinear scattering effect during pixel construction process.
Nareenart Raksuntorn, Qian Du 0001
openaire   +2 more sources

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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Estimate the Number of Endmembers for Hyperspectral Imagery

2009 International Conference on Environmental Science and Information Application Technology, 2009
In practice, the determination of the number of endmembers for hyperspectral images of the areas without priori knowledge is highly difficult. This article brings forward an automatic method, which can estimate the number of endmembers for hyperspectral imagery without priori knowledge of the area, according to the theory of Orthogonal subspace ...
Wei Chen, Xu-chu Yu, He Wang
openaire   +1 more source

Anomaly detection and classification for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2002
Anomaly detection becomes increasingly important in hyperspectral image analysis, since hyperspectral imagers can now uncover many material substances which were previously unresolved by multispectral sensors. Two types of anomaly detection are of interest and considered in this paper. One was previously developed by Reed and Yu to detect targets whose
Chein-I Chang, Shao-Shan Chiang
openaire   +1 more source

Band reduction for hyperspectral imagery processing

SPIE Proceedings, 2010
Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation.
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Detection of underwater objects in hyperspectral imagery

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
One of the biggest challenges in detecting underwater objects in hyperspectral imagery is that, unlike the land-based case, the observed spectrum of an underwater target is highly dependent on the properties of the surrounding water, as well as the depth of the target. In this paper we present a very general framework for underwater detection.
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