A hybrid classifier for remote sensing applications.

G S Ruppert; M Schardt; G Balzuweit; M Hussain

doi:10.1142/s0129065797000094

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A hybrid classifier for remote sensing applications.

G S Ruppert·M Schardt·G Balzuweit·M Hussain

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Abstract

This paper presents a hybrid-unsupervised and supervised-classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptions while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with complex non-normally distributed classes than Maximum Likelihood. The main advantage of the Neural Gas classifier, however, is that it requires much less user interaction than other classifiers, especially Maximum Likelihood.

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Algorithms Automation Classification Computers, Hybrid Environmental Monitoring Likelihood Functions Reproducibility of Results Telemetry Journal Article

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BibTeX RIS

G S Ruppert, M Schardt, G Balzuweit, M Hussain. 1997. A hybrid classifier for remote sensing applications.. https://doi.org/10.1142/s0129065797000094

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