Increasing the accuracy of thematic maps generated
using satellite imagery is a crucial task in remote sensing. In
this study, a product-level fusion (PLF) approach based on
integration of different land-type maps generated using various
satellite-derived indices including normalized difference
water index (NDWI), normalized difference built-up index
(NDBI), enhanced vegetation index (EVI), and normalized
difference vegetation index (NDVI) is proposed to improve
the accuracy of land cover mapping. The suitability of the
proposed approach for land cover mapping is evaluated in
comparison with two high-performance image classification
techniques including support vector machine (SVM) and artificial
neural network (ANN). The results show that the overall
accuracy and kappa values of about 95.95 % and 0.95,
94.91 % and 0.94, and 85.32 % and 0.82 are achieved for
the PLF, SVM, and ANN approaches, respectively. The results
indicate superiority of the PLF approach than SVM and
ANN techniques for land cover classification of Advanced
Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) imagery, especially for the extraction of forest, rice,
and citrus classes. However, SVM technique also provided
reliable result for land cover mapping.