Results 181 to 190 of about 835 (219)

Improving N-Finder technique for extracting endmembers

2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2011
N-FINDER algorithm is widely used for endmember extraction. One of the disadvantages of N-FINDER is that its implementations take long run time due to the relatively large computational complexity of N-FINDER. Successfully reducing the size of the input data set -the hyperspectral image - that the algorithm works on can reduce the overall run time of ...
Mahmoud Maghrbay, Reda A. Ammar, Sanguthevar Rajasekaran   +2 more
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An optimization perspective onwinter's endmember extraction belief

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
In this paper, we describe a continuous optimization perspective on Winter's simplex volume maximization belief for endmember extraction in hyperspectral remote sensing. Winter's belief, proposed in the late 90's, is very insightful and has led to one of the most widely used class of endmember extraction algorithms nowadays—N-FINDR.
Tsung-Han Chan, Wing-Kin Ma, Arul-Murugan Ambikapathi, Chong-Yung Chi   +3 more
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Compressive sensing for endmember extraction

2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016
With constant improvement in spatial resolution of satellite sensors, the ability to eliminate the spectral secondary reflection effect is continuously enhanced, which brings new research opportunities for hyperspectral image classification, but mixed pixel decomposition of hyperspectral image has become a new research difficulty in the field of remote
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Spectral curve-based endmember extraction method

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
Most commonly-used methods to determine the number of endmembers and to extract endmember spectra are affected by spectral variability caused by variations in illumination or the physical characteristics of materials. This paper proposes a novel endmember extraction method which can consider the spectral variability.
Tatsumi Uezato, Richard J. Murphy, Arman Melkumyan, Anna Chlingaryan   +3 more
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An endmember extraction framework based on abundance constraint

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012
Spectral unmixing is an important technique for hyperspectral data interpretation, in which a mixed spectral signature is decomposed into a collection of spectrally constituent and pure spectra, called endmembers, and a set of correspondent fractions, or abundances, that indicate the proportion of each endmember's presence in the mixture.
Mingming Xu 0001, Liangpei Zhang 0001, Bo Du 0001, Liqun Liu   +3 more
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Endmember Extraction Technique of HSI

2015
Before establishing the linear mixed model and conducting the spectral unmixing operation, it is very necessary to extract the spectral endmember, which acquires the essential priori information for the spectral unmixing.
Liguo Wang, Chunhui Zhao
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An Improved Endmember Extraction Using Evolutionary Approach

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), 2018
There are two motivations to apply swarm knowledge hypothesis for the end member extraction i.e., it’s great execution without backtracking and its low numerical tractability. The proposed methodology is an enhanced endmember extraction calculation with leeway of the high dimensional simplex and proficient improvement where molecule swarm intelligence ...
Omprakash W. Tembhurne, Deepti D. Shrimankar, Nagaraj V. Dharwadkar, Manish Paliwal   +3 more
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Impact of Initialization on Design of Endmember Extraction Algorithms

IEEE Transactions on Geoscience and Remote Sensing, 2006
Many endmember extraction algorithms (EEAs) have been developed to find endmembers that are assumed to be pure signatures in hyperspectral data. However, two issues arising in EEAs have not been addressed: one is the knowledge of the number of endmembers that must be provided a priori, and the other is the initialization of EEAs, where most EEAs ...
Antonio Plaza, Chein-I Chang
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Support Vector Machine-Based Endmember Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2009
Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions.
Anthony M. Filippi, Rick Archibald
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Using principal component analysis for endmember extraction

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
This paper introduces a new simplex-based unsupervised endmember extraction method from hyperspectral data. The method exploits the dimensionality reduction ability of the principal component analysis, and generalizes the concept that, the first generated endmember by the Simplex Growing Algorithm, is always a pixel which has either a maximum or a ...
Charoula Andreou, Vassilia Karathanassi
openaire   +1 more source