There are few earlier studies that show the utility of ancillary and geographically derived data for improving LC classification. Na et al., (2010) [21] used 103 geographical layers to show improvement in LC mapping using Landsat TM bands 1 to 5 and 7, NDVI, EVI, a data fusion transformation combining the six bands information from the Landsat TM image (first principal component – PC1) as additional predictors, image texture measures (variance, homogeneity, contrast, dissimilarity and entropy) with window size of 3 x 3 pixels and 11 x 11 pixels, DEM, slope and soil type with Random Forest (RF), Classification and Regression Tree (CART) and Maximum Likelihood Classifier (MLC) based classification. Among these, RF yielded accurate classification with an overall accuracy of 91% and kappa 0.89. They also quantified the effect of training set size on the performance of classification algorithms. Xiaodong et al., (2009)[22] integrated TM data with NDVI, EVI, first principal component (PC), slope, soil types and five texture measures (variance, homogeneity, contrast, dissimilarity and entropy) for LC classification of Marsh Area using CART and MLC. They concluded that image spectral, textural, terrain data and ancillary Geographical Information System (GIS) improved the land use and land cover (LULC) classification accuracy significantly. Fahsi et al., (2000)[23] evaluated the contribution and quantified the effectiveness of DEM in improving LC classification using Landsat TM data over a rugged area in the Atlas Mountains, Morocco. The study showed that DEM considerably improved the classification accuracy by reducing the effect of relief on satellite images, increasing the individual accuracies of the different classes by upto 60%. 

Recio et al., (2011)[24] used historical land use (LU) and ancillary data as a feature in a geospatial framework for image classification and showed improvement in overall classification accuracy considered case-by-case for each class. Masocha and Skidmore (2011)[25] used DEM along with ASTER imagery and georeferenced point data obtained from field to increase the accuracy of invasive species (Lantana camera) mapping. They used Neural Network and SVM classifiers along with GIS expert system to develop hybrid classifiers. The overall accuracy increased from 71% (kappa 0.61) to 83% (kappa 0.77) with Neural Network and from 64% (kappa 0.52) to 76% (kappa 0.67) with SVM hybrid classifiers. Dorren et al., (2003)[26] studied the effect of topographic correction and the role of DEM as additional band using per-pixel and object based classification to classify forest stand type maps using Landsat TM data in a steep mountainous terrain. They concluded that both topographic correction and classification with DEM as additional band increased the overall accuracy.

Xian et al., (2008)[27] quantified multi-temporal urban development characteristics in Las Vegas from Landsat and ASTER Data. Apart from the satellite imageries, NDVI, slope, aspect and temperature were used for classification. Lu and Weng (2005)[9] demonstrated urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Role of PC’s of ETM+ MS bands, texture, temperature and data fusion of MS and PAN to improve classification accuracy were considered. They concluded that texture and temperature may improve classification accuracy for some classes, but may degrade for other classes. Data fusion of MS and PAN are useful but high spatial resolution also increases spectral variation within the classes, decreasing the classification accuracy. Data fusion combined with texture significantly improved classification accuracy.