Larsemann Hills, Antarctica

SVM, MXL, NNC, SAM and Winner Takes All (WTA)

Results indicate that the WTA integration and the SVM classification methods were more accurate than the MXL, NNC, and SAM classification methods.

QB and WV-2


São Paulo, Brazil

DT, RF, SVM, Regression tree (RT)

RF achieved the highest accuracy (κ = 0.95), followed either by the RT (κ = 0.85) or the DT (κ = 0.77). The SVM, maybe due to the high dimensionality and over-fitting issues, was the algorithm that performed the worst.

GE-1 and WV-2


Cuevas del Almanzora, southern Spain


The overall accuracy attained by applying NN and SVM to the four MS bands of GE-1 were very similar to those computed from WV-2, for either four or eight MS bands. The best overall accuracy values were close to 90%, and they were not improved by using multi-angle ortho-images.

GE-1 and QB


Fredericton, Canada

FM and CM

The overall accuracies using FM stand higher than those of CM. The overall accuracy and kappa coefficient for QB image classification was better than that of the GE-1 image.




OOC using mathematical and morphology analysis

OOC based on multi-resolution segmentation and mathematical morphology analysis procedures performs best with a spatial accuracy above 85% and a statistical accuracy above 97%.



Nile river, Egypt

Contextual classifier, MXL and MD

The MXL classifier yielded the best classification accuracy (up to 97%) compared to the other two classifiers.

SPOT and Landsat TM


Northern Territory Tropical Savanna, Australia

Supervised image classification with and without ancillary data―NDVI, DEM, slope model & hydrology.

The producer accuracy on average (40%) was higher for the image classification (without ancillary data) and a marginal difference in user accuracy (5%). For the integrated approach (image plus ancillary data) producer and user accuracies were 34% and 35% respectively.



East Sikkim, India

BAN and hybrid classification.

Overall accuracy was found to be 90.53% using the BAN classifier and 91.57% using the Hybrid classifier.



Beijing, China.


The best mean overall classification accuracy is 86.2% (BAN). As expected, BAN gives better classification results than NBC.



University Putra,


OOC including fuzzy rule-based and SVM

Classification result of supervised SVM contained mixed objects and misclassifications of impervious surfaces and other urban features. Rule-based classifier (overall accuracy = 93.07%) performed better than supervised SVM (overall accuracy = 85.02%) resulting in finer discrimination of spatially and spectrally similar objects.