Semi-supervised learning with constrained virtual support vector machines for classification of remote sensing image data

Semi-supervised learning with constrained virtual support vector machines for classification of remote sensing image data

We introduce two semi-supervised models for the classification of remote sensing image data. The models are built upon the framework of Virtual Support Vector Machines (VSVM). Generally, VSVM follow a two-step learning procedure: A Support Vector Machines (SVM) model is learned to determine and extr...

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Bibliographic Details
Main Authors: Christian Geiß, Patrick Aravena Pelizari, Ozan Tunçbilek, Hannes Taubenböck
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Image classification
Virtual support vector machines
Semi-supervised models
Self-learning
Active learning model heuristics
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843223003953

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