| Satellite Image | Study Area (Application) | Classifiers | Classes | Accuracy/Results |
| Geo Eye-1 (GE1) [26] | Beijing, China. (Land use/cover) | BAN and NBC | 5 [Houses, Roads, Grass, Hills, and Rivers] | BAN-86.2% NBC-82.0% |
| GE-1 and QB [27] | Fredericton, Canada. (Land use Land cover) | Fuzzy method (FM) and Crisp method (CM) | 5 [Shadow, vegetation, road, building and bare land] | FM (GE-1)-82% FM(QB)-90% CM (GE-1)-68% CM (QB)-42% |
| GE-1 and WV-2 [28] | Cuevas del Almanzora, southern Spain (Land cover classification) | OOC-NNC and SVM | 6 [Greenhouses, Nets, Vegetation, Orchards, Buildings, Bare soil] | NNC (GE-1)-87.91% SVM (GE-1)-85.71% NNC (WV-2_4)-89.01% SVM (WV-2_4)-84.07% NNC (WV-2_8)-87.91% SVM (WV-2_8)-87.36% |
| IKONOS [29] | Ghent in Belgium (Urban land cover mapping) | Ensemble Classifiers-DT, ANN, and RF. | 9 [water, grass, trees, buildings (with dark roof, red roof, bright roof), roads, other man-made objects, shadow] | Results indicate that ensemble classifiers generate significantly higher accuracies than a single classifier. |
| IKONOS [30] | Pico da Vara Natural Reserve. (Vegetation mapping) | SVM, ANN, MhD and MXL (parametric methods) | 8 [Forestry production species, aggressive alien invasive species, bare soil areas, clouds, natural pasture areas and shadows of clouds] | Despite the poor separability of some vegetation categories, MXL, SVM and ANN classifications have achieved good overall accuracies (overall accuracy > 75% and Kappa Index Agreement > 0.6). |
| QB [31] | Brandenburg, Germany. (Forest types) | Knowledge-based methods | 5 [Pine, Larch, Beech, Robinia and Oak] | Results show a good separability with approximately 80% to 90% overall accuracy for the tree species beech, oak, robinia, larch, and pine. |
| QB [32] | Lang Tengah Island. (Coral distribution mapping) | Ensembles classifier-PP, MD, MXL, Fisher and K-Nearest Neighbor (NN) | 4 [Dense coral, Sparse Coral, Dead Coral and Sand] | Using an ensemble classification approach, highest overall accuracy (73.02%) was seen in comparison to PP (52.38%), MD (50.79%), MXL (60.37%), Fisher (31.75%) and K-NN (50.79%) |
| WV-2 [33] | São Luís, Brazil. (Classification of Mangrove Areas) | OOC | 8 [Streets, Tidal flat, Tidal channel, ceramic roof, asbestos roof, metal roof, mangroves, no-mangroves] | Kappa index value of 0.93 was found for the generated maps |
| WV-2 [34] | Zhengzhou city, China. (Urban land cover) | OOC | 5 [Vegetation, water, road, building, space land] | 84.3144% Kappa = 0.7807 |
| SPOT [35] | Central-north Poland. (Land cover mapping) | Rule-based classification-OOC approach | 13 [Continuous built-up land, discontinuous built-up land, industrial units, construction sites, green urban areas, arable land, grasslands, gardens, coniferous forests, deciduous forests, mixed forests, deforestations and water] | Overall accuracy-89.1% Kappa coefficient-0.87 |