Results 181 to 190 of about 1,111,541 (211)

Spatial constraints on endmember extraction and optimization of per-pixel endmember sets for spectral unmixing

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the spectral endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the mixing process.
Benoit Rivard, Derek M. Rogge, Jilu Feng, J. Zhang   +3 more
openaire   +2 more sources

The extension of endmember extraction to multispectral scenes

SPIE Proceedings, 2004
A multiple simplex endmember extraction method has been developed. Unlike convex methods that rely on a single simplex, the number of endmembers is not restricted by the number of linearly independent spectral channels. The endmembers are identified as the extreme points in the data set. The algorithm for finding the endmembers can simultaneously find
John H. Gruninger, Anthony J. Ratkowski, Michael L. Hoke   +2 more
openaire   +1 more source

A New Preprocessing Technique for Fast Hyperspectral Endmember Extraction

IEEE Geoscience and Remote Sensing Letters, 2013
Q1
Sebastián López, Javier F. Moure, Antonio Plaza, Gustavo M. Callicó, José Francisco López, Roberto Sarmiento   +5 more
openaire   +3 more sources

Comparison of hyperspectral endmember extraction algorithms

Journal of Applied Remote Sensing, 2013
In hyperspectral imagery, endmember extraction is the process of finding a pure spectrum set within the materials present in a hyperspectral scene. However, various endmember extraction algorithms (EEAs) can yield different endmember spectrum sets. This research presents a comparison of four EEAs: pixel purity index, automatic target generation process
Jee-cheng Wu, Gwo-chyang Tsuei
openaire   +1 more source

Extraction of Endmembers from Spectral Mixtures

Remote Sensing of Environment, 1999
Abstract Linear spectral mixture modeling (LSMM) divides each ground resolution element into its constituent materials using endmembers which represent the spectral characteristics of the cover types. However, it is difficult to identify and estimate the spectral signature of pure components or endmembers which form the scene, since they vary with ...
F.J. Garcı́a-Haro, M.A. Gilabert, J. Meliá   +2 more
openaire   +1 more source

Algorithm Research on Endmember Extraction Combined With Distribution Statistics

2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018
The spatial resolution of hyperspectral sensor is limited and the surface features are complicated, and each pixel contains more material information, resulting in the existence of a large number of mixed pixels. Therefore, the research on endmember extraction method have been becoming a hotspot in the hyperspectral field.
Meiping Song, Ming Xu, Chein-I Chang
openaire   +2 more sources

Statistical convex partitioning for endmember extraction

SPIE Proceedings, 2010
Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a hyperspectral scene. Most of the spectral-based endmember extraction methods relay on the ability to discriminate between pixels based on their spectral characteristics and the assumption that pure pixels exist in the image.
openaire   +1 more source

Endmember Extraction

2022
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Multi-Fidelity Evolutionary Multitasking Optimization for Hyperspectral Endmember Extraction

Applied Soft Computing Journal, 2021
Maoguo Gong, Hao Li, Jianzhao Li
exaly  

Endmember Extraction without prior Number of Endmembers using Barycentric Abundances

2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 2023
Gouri Shankar Chetia, Bishnulatpam Pushpa Devi   +1 more
openaire   +1 more source