Unsupervised clustering and spectral unmixing for feature extraction prior to supervised classification of hyperspectral images

2011 ◽  
Author(s):  
Inmaculada Dópido ◽  
Alberto Villa ◽  
Antonio Plaza
Keyword(s):  
Feature Extraction ◽  
Supervised Classification ◽  
Unsupervised Clustering ◽  
Spectral Unmixing ◽  
Hyperspectral Images

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

2016 ◽  
Vol 54 (6) ◽  
pp. 3410-3420 ◽  
Author(s):  
Frank de Morsier ◽  
Maurice Borgeaud ◽  
Volker Gass ◽  
Jean-Philippe Thiran ◽  
Devis Tuia
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Sparse Graph

Machine learning reveals multiple classes of diamond nanoparticles

2020 ◽  
Vol 5 (10) ◽  
pp. 1394-1399
Author(s):  
Amanda J. Parker ◽  
Amanda S. Barnard
Keyword(s):  
Machine Learning ◽  
Electronic Properties ◽  
Supervised Classification ◽  
Unsupervised Clustering ◽  
Diamond Nanoparticles ◽  
Different Characteristics

Unsupervised clustering and supervised classification of a diverse set of reconstructed, twinned and passivated diamond nanoparticles predict nine classes that have distinctly different characteristics and electronic properties.

scholarly journals Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering

2018 ◽  
Vol 10 (4) ◽  
pp. 515 ◽  
Author(s):  
Binge Cui ◽  
Xiaoyun Xie ◽  
Siyuan Hao ◽  
Jiandi Cui ◽  
Yan Lu
Keyword(s):  
Supervised Classification ◽  
Label Propagation ◽  
Hyperspectral Images
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