Results 121 to 130 of about 763 (175)

Superpixel Endmember Detection

IEEE Transactions on Geoscience and Remote Sensing, 2010
Superpixels are homogeneous image regions comprised of multiple contiguous pixels. Superpixel representations can reduce noise in hyperspectral images by exploiting the spatial contiguity of scene features. This paper combines superpixels with endmember extraction to produce concise mineralogical summaries that assist in browsing large image catalogs ...
David R. Thompson 0001, Lukas Mandrake, Martha S. Gilmore, Rebecca Castaño   +3 more
openaire   +1 more source

Influence of the Darkest Pixel on Endmembers Initialization

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Endmember extraction is one of the necessary steps in hyperspectral data investigation. The eventual objective of hyperspectral data processing and analysis is to improve the accuracy of target identification, and the precise identification of endmembers is a challenging task.
Palla Parasuram Yadav, Amba Shetty, B. S. Raghavendra, A. V. Narasimhadhan   +3 more
openaire   +1 more source

Endmember orthonormal mapping in hyperspectral mixture analysis to address endmember variability

Earth Science Informatics, 2016
Spectral unmixing estimates the abundance of each endmember at every pixel of a hyperspectral image. Each material in traditional unmixing algorithms is represented through a constant spectral signature. However, endmember variability always exists due to environmental, atmospheric, and temporal conditions, which leads to poor accuracy of the estimated
Ali Jafari, Reza Safabakhsh, Mohammad Mehdi Ebadzadeh   +2 more
openaire   +1 more source

Spatial Preprocessing for Endmember Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2009
Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on
Maciel Zortea, Antonio Plaza
openaire   +1 more source

L1-endmembers: a robust endmember detection and spectral unmixing algorithm

SPIE Proceedings, 2010
A hyperspectral endmember detection and spectral unmixing algorithm based on an l 1 norm factorization of the input hyperspectral data is developed and compared to a method based on l 2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the l 1 norm and the SPICE algorithm based on the l 2 norm, simultaneously and ...
Alina Zare, Paul Gader
openaire   +1 more source

Improving the quality of extracted endmembers

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinctive pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature ...
Qian Du 0001, Liangpei Zhang 0001, Nareenart Raksuntorn   +2 more
openaire   +1 more source

Spectral-textural endmember extraction

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information ...
Maciel Zortea, Devis Tuia, Fabio Pacifici, Antonio J. Plaza   +3 more
openaire   +1 more source

Sparse endmember extraction and demixing

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
A novel algorithm for endmember extraction is presented. The approach follows the linear mixture model for hyperspectral data. Endmembers are identified based on sparsity consideration. Theoretical and experimental results suggest the potential of the method.
Martin Ehler, Matthew J. Hirn
openaire   +2 more sources

PCE: Piecewise Convex Endmember Detection [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2010
A new hyperspectral endmember detection method that represents endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions (EDs) and proportion values for each convex region is presented.
Alina Zare, Paul Gader
exaly   +2 more sources

Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis

IEEE Transactions on Geoscience and Remote Sensing, 2000
Accuracy of vegetation cover fractions, computed with spectral mixture analysis, may be compromised by variation in canopy structure and biochemistry when a single endmember represents top-of-canopy reflectance. In this article, endmember variability is incorporated into mixture analysis by representing each endmember by a set or bundle of spectra ...
C. Ann Bateson, Gregory P. Asner, Carol A. Wessman   +2 more
openaire   +1 more source