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