Feature Extraction of Hyperspectral Images With Semisupervised Graph Learning

We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2016-09, Vol.9 (9), p.4389-4399
Main Authors: Luo, Renbo, Liao, Wenzhi, Huang, Xin, Pi, Youguo, Philips, Wilfried
Format: Article
Language:English
Subjects:
Classification
Correlation
Feature extraction
graph
hyperspectral images (HSIs)
Hyperspectral imaging
Image edge detection
semisupervised
Symmetric matrices
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Summary:We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised graph, we connect labeled samples according to their label information and unlabeled samples by their nearest neighborhood information. By sorting the mean distance between a unlabeled sample and labeled samples of each class, we connect the unlabeled sample with all labeled samples belonging to its nearest neighborhood class. Moreover, the proposed SEGL better models the actual differences and similarities between samples, by setting different weights to the edges of connected samples. Experimental results on four real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2016.2522564