Satellite Data	Classifiers	Factors Affecting Information Extraction
GE-1 and QB [27]	FM and CM	The presented method performed quite well in vegetation detection, however, the CM missed to classify a huge number of segments related to vegetation. Since this method was very good at detecting vegetation, the overall accuracy and kappa coefficient for QB image was higher than that of the GE-1 image.
GE-1 and WV-2 [28]	OOC analysis-NN and SVM	Classification algorithms frequently used in OOC approaches, as is the case of NN, do not perform well on a high-dimensional feature space, due to problems related to feature correlation. On the whole, NN performed quite well with bare soil, greenhouses and nets, whereas SVM only outperformed NN in the case of the building class. However, the performance of SVM when classifying vegetation and orchards was quite poor. The shape and geometric features (B + Sh), ratios to the scene (B + Rs) and texture features based on GLCM (B + T) did not contribute to improving the classification.
IKONOS [29]	Ensemble Classifiers-DT, ANN, Adaboost with DT as base-classifier and RF, which uses a CART like DT	Ensemble classifier brought an increase of 6% - 12%. ANN shows better results where it outperforms a DT by more than 8%, but only RF has a higher accuracy (+4%) than ANN. For all ensemble classifications using DT as the base learner, the computing time is still very fast even with hundreds of classifiers more. Hence, it would be beneficial to implement binary strategies with ensemble classifiers. (This, however, does not apply to ANN for which training costs will be substantially higher and easily become prohibitive).
IKONOS [30]	SVM, ANN, MhD, and MXL	Non-parametric methods SVM and ANN performed better than parametric methods (MXL and MhD), mostly for the less separable and heterogeneous classes. When classifying complex data sets, SVM and NN appear to be better options because they don’t assume any data distribution while MXL should be used with good results when the data distribution is Gaussian. Classifications using SVM, ANN or MXL techniques could be improved by increasing the quantity and quality of training sites.
QB [31]	Knowledge-based methods	Suitable training pixels can improve the overall accuracy to κ = 0.95. Mistakes in the assignment mainly occur with pixels close to the border to the next age class. The use of knowledge bases and additional data increases the number of separable classes and leads to better results than simple supervised or unsupervised classifications. A major problem is that classes which are not considered in the knowledge base will introduce mistakes into the classification. Usually, most pixels that are assigned as training pixels are classified correctly and only a negligible minority is misclassified.
QB [32]	Ensembles classifier-PP, MD, MXL, Fisher and K-NN	The output maps from different supervised techniques had an overall moderate accuracy with a range from 41% to 56%. Although such results are only moderate by statistical standards, these are not unrealistic values, especially in RS of marine environments as this could be caused by a number of factors such as the delay in the time of ground truthing and the time when the satellite images were taken and the water column correction algorithm may not remove the effects of water attenuation completely.
WV-2 [33]	OOC	The result of the scene classified was improved because the attributes related to both channels Yellow and Red-Edge are better defined, eliminating confusions which occurred in the past, for instance, with classes Ceramic roof and Bare soil. This is due to the fact that this channel is positioned spectrally at the absorption end from the red and beginning of the infrared wavelength for part of the vegetation. So it is partially sensitive to the spectral behavior of different vegetation types from this region, which was expected for the spectral bands of this satellite system.
WV-2 [34]	OOC	The classification method and corresponding classification parameter are low in flexibility and portability; it is difficult to solve the influence of the shadow of buildings and crown in classification.
SPOT [35]	Rule-based classification-OOC	The overall accuracy of classification was 89.1%. Main misclassifications are due to confusion with two classes: mixed forests and arable land. The lowest accuracy was reached for class “gardens”; although producer’s accuracy is 100%, user’s accuracy reaches only 42.5%. It means that too many objects were attributed to this class. In case of this class, the applied classification approach proved to be not sufficient. It is caused by spectral heterogeneity of class “gardens” and spatial resolution of SPOT image, which is not adequate for recognizing texture features formed by relatively small objects.