A hybrid classifier for remote sensing applications.
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.
Ruppert
G S
GS
Institute of Image Processing, Joanneum Research, Graz, Austria. georg.ruppert@joanneum.ac.at
Schardt
M
M
Balzuweit
G
G
Hussain
M
M
D000465:Algorithms; D001331:Automation; D002965:Classification; D003203:Computers, Hybrid; D004784:Environmental Monitoring / Q000379:methods*; D016013:Likelihood Functions; D015203:Reproducibility of Results; D013686:Telemetry