Analysis of the role of ancillary and derived geographical layers in improving classification accuracy is required along with the new methods of expert classification being developed [25]. In this work, derived and ancillary layers were assessed for their performance in improving classification accuracy in an urbanised landscape.

The results provided new insights to the likelihood of improved performance of LC classification by use of supplemental layers related to the region along with the RS data. IKONOS data were used for urban area classification along with many other layers of elevation and texture. The enhanced characteristics of IKONOS MS and PAN compared to Landsat ETM+ highlighted some typical urban features such as buildings and narrow roads in residential areas compared to the latter (figure 3). IKONOS image not only reduced the mixed pixel problem, but also provided a rich texture and contextual information than Landsat ETM+ MS bands with 15 or 30 m spatial resolution. Earlier works [33-34] have used SPOT HRV data for urban classification due to its high spatial resolution comparing with Landsat ETM data. Knowledge based expert system have also been used with MS imagery and LiDAR data to delineate impervious surface in urban areas [35]. The study showed that high spatial resolution is considered to be more important than high spectral resolution in urban classification [1].

In IKONOS data classification, when only DEM and texture measures were added as input to the classifier apart from IKONOS 4 spectral bands, the overall accuracy went high to 88.72% (3.5% improvement) with high producer’s and user’s accuracies for individual classes, which is comparable to the overall accuracy obtained by classifying QuickBird imagery for LC classification in a complex urban environment based on texture (overall accuracy – 87.33%) and segmentation (overall accuracy – 88.33%) by Lu et al., (2010) [14]. Although it is difficult to identify suitable texture which is dependent on image band and window size for the specific study [36], appropriate texture measures reduce spectral variation within same LC and also improves spectral separability among different LC classes [37-40]. It is to be noted that derived layers from elevation such as slope and aspect did not aid in discriminating classes and EVI did not prove useful in classification due to poor vegetation cover. Addition of temperature, NDVI, EVI, elevation, slope, aspect, PAN and texture with Landsat ETM+ spectral bands 1, 2, 3, 4, 5 and 7, significantly improved the classification accuracy by 7.6%, which proved to be useful with medium spatial resolution data in an urban area.

However, in addition to the use of ancillary layers such as textural images, selection of different seasonal images along with suitable classification algorithms are also needed to improve classification performance [14].