Semi-Supervised Graph-Based Hyperspectral Image Classification

G. Camps-Valls; T. Bandos Marsheva; Dengyong Zhou

doi:https://doi.org/10.1109/tgrs.2007.895416

doi.org/10.1109/tgrs.2007.895416

Semi-Supervised Graph-Based Hyperspectral Image Classification

,

,

IEEE Transactions on Geoscience and Remote Sensing8.20

Volume: 45, Issue: 10, Pages: 3044 - 3054

Published: Oct 1, 2007

Abstract

This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to handle the special characteristics of hyperspectral images, namely, high-input dimension of pixels, low number of labeled samples, and spatial variability of the spectral signature. To alleviate these problems, the method...

Paper Fields

Paper Details

Title

Semi-Supervised Graph-Based Hyperspectral Image Classification

DOI

doi.org/10.1109/tgrs.2007.895416

Published Date

Oct 1, 2007

Journal

IEEE Transactions on Geoscience and Remote Sensing

Volume

45

Issue

10

Pages

3044 - 3054

Citation AnalysisPro

You’ll need to upgrade your plan to Pro

Looking to understand the true influence of a researcher’s work across journals & affiliations?

Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.

Learn more

Notes

History