Improvement of Image Classification with the Integration of Topographical Data

Remotely sensed data are essentially used for land cover and vegetation classification. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. One of the most common attempts to improve image classification is the integration of ancillary data into classification. In this study, an approach for integrating topographic data into land cover classification is presented. Integration is basically through selection of training set in order to provide additional sensitivity to topographical characteristics associated with each land cover class in the study area. Topographic data including elevation, slope and aspect are tested for their correlation with land cover classes and correlated topographic data are used as input. Signatures from topographical data are assumed to represent the topographical preferences of land cover classes and are extracted with respect to the spatial position of spectral signatures from the remotely sensed images. Initial set of topographical signatures is evaluated and refined statistically. A new training set covering both spectral and the topographical signatures is created. New training set is used to supervise the standard Maximum Likelihood classification where; topographical raster data together with images is used as input for the classification. Two products are derived. First product used remotely sensed data only as input and is trained by spectral information. Second product used bands and topographical data as input and it is trained with both spectral and topographical information. Comparison between two products conveyed that procedure provided an improvement of 10% in overall accuracy for the classification with the integration of topographical data over the one that depended on spectral data only.