Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification

Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data....

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Bibliographic Details
Published inIEEE access Vol. 11; pp. 124819 - 124832
Main Authors Georgoulas, Ioannis, Protopapadakis, Eftychios, Makantasis, Konstantinos, Seychell, Dylan, Doulamis, Anastasios, Doulamis, Nikolaos
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Annotations
Classification
Classification algorithms
Data models
Feature extraction
Graph-based
hyperspectral data
Hyperspectral imaging
Image analysis
Machine learning
Mathematical analysis
Neural networks
Remote sensing
Semi-supervised learning
Semisupervised learning
Task analysis
tensor-based embedding
Tensors
Unmanned aerial vehicles
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Summary:Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data. Due to recent advances in hyperspectral imaging and autonomous (unmanned) aerial vehicles collecting new hyperspectral data is an easy task. Annotating those data, however, is a tedious, time-consuming and costly task requiring the in-situ presence of human experts. One way to loosen the requirement of a large number of annotated data is the shift to semi-supervised learning combined with highly sample-efficient tensor-based neural networks. This study provides a comprehensive experimental analysis of the performance of a variety of graph-based semi-supervised learning techniques combined with tensor-based neural network embeddings for the problem of hyperspectral data classification. Experimental results suggest that the combination of tensor-based neural network embeddings with graph-based semi-supervised learning can significantly improve the classification results minimizing human annotation effort.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3328388