Cited by 54Remote-Sensing Image ClassificationMachine Learning and ELMAdvanced Image Fusion Techniques

Exploring ELM-based spatial–spectral classification of hyperspectral images

Heras, Dora B., Argüello, Francisco, Quesada-Barriuso, Pablo·2014·International Journal of Remote Sensing

Abstract

Among the different computational intelligence techniques avalaible for hyperspectral data classification, support vector machines (SVMs) have played a dominant role. Recently, a new learning algorithm for single-layer feedforward neural networks called the extreme learning machine (ELM) was proposed. This technique is competitive with SVMs in terms of accuracy, learning speed, and computational scalability. In this article, we propose and evaluate the use of ELM for land-cover classification from hyperspectral images. In addition, we consider two ELM-based techniques integrating spectral and spatial information of the image. The first is a scheme that uses a majority vote approach in order to combine the results of a pixel-wise spectral classification by ELM and a segmentation map obtained by a watershed algorithm. The second introduces spatial information from a small spatial neighbourhood after the classification by ELM. We show the usefulness of spatial–spectral ELM-based classification techniques in hyperspectral imaging. The results are compared to those obtained by similar SVM-based techniques and show improved classification results and much lower execution time. These simple and computationally cheap methods can be combined with others traditionally applied to hyperspectral images.

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Crossref · 10.1080/01431161.2013.869633

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  • The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM), partial least squares discriminant analysis (PLS-DA), and various deep learning algorithms.

    “The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM) [7–9], partial least squares discriminant analysis (PLS-DA) [10,11], and various deep learning algorithms [12,13].”

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Authors
Heras, D. B., Argüello, F., & Quesada-Barriuso, P.
Affiliations
CITIUS (Centro Singular de Investigación en Tecnoloxías da Información), Rúa de Jenaro de la Fuente Domínguez, 15782 Santiago de Compostela, Spain
Corresponding author
Heras, Dora B.
Venue
International Journal of Remote Sensing
Published
2014-01-02
Volume
35
Issue
2
Pages
401-423
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W2011731909
Cited by
54
References
50
Citations by year
2026: 1 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 7 · 2017: 7 · 2016: 9 · 2015: 6 · 2014: 3
FWCI
12.72
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Remote Sensing (publishedVersion)
Topics
  • Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
Keywords
extreme learning machine, hyperspectral image classification, land cover classification, watershed segmentation, majority voting, single-layer feedforward neural network, support vector machine, computational efficiency

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