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Parallel Collaborative Filtering Recommendation Model Based on Two-Phase Similarity Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Supervised Feature Extraction of Hyperspectral Image by Preserving Spatial-Spectral and Local Topology

Authors : Peng Zhang, Haixia He, Zhou Sun, Chunbo Fan

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

Manifold learning, as a promising tool for nonlinear dimensionality reduction of hyperspectral image (HSI) data, has drawn great research interests in the remote sensing community. It can extract meaningful and low-dimensional features underlying complex HSI data, which is useful in classification of ground targets. However, there are two limitations with current approaches, few considerations of spatial information and lack of explicit mapping relationship. In this paper, we propose a supervised spatial-spectral local topology preserving embedding (sssLTPE) method for efficient feature extraction of HSI, which owns two merits. First, spatial and spectral information at each pixel is integrated by an intuitive strategy. Second, an explicit and nonlinear mapping relationship is provided to effectively map unlabeled data to learned feature space. Experiments conducted on benchmark data set demonstrate that high classification accuracy can be obtained by using the features extracted by sssLTPE.

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Metadata
Title
Supervised Feature Extraction of Hyperspectral Image by Preserving Spatial-Spectral and Local Topology
Authors
Peng Zhang
Haixia He
Zhou Sun
Chunbo Fan
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_68

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