Extracting River Features from Remotely Sensed Data: An Evaluation of Thematic Correctness. GI_Forum 2013 – Creating the GISociety|

Automatic feature extraction from satellite imagery is cost effective and fast. An essential issue in this context is the degree of accuracy for thematic correctness obtainable through common pixel-based and object-oriented classification algorithms. By applying two classification algorithms to Landsat 5 TM imagery for the extraction of different morphological river features the thematic correctness of the resulting raster images and the separability of the river features is evaluated. River features of meandering rivers evolve through dynamic avulsion, erosion and deposition processes. Although many studies focus on the analysis of these river environments, diverse methods of GIS and remote sensing based river feature classification methods have not been evaluated and assessed yet. In the literature several techniques to monitor spatio-temporal changes such as lateral river channel migration are already mentioned but the tendency there is to identify the changes by examining time spans rather than a point in time. Besides that the semiautomatic river feature methods described in related studies mainly focus on the identification of a river channel itself and do not consider additional features such as oxbows, scars, relic channels, etc. that in fact are significant characters in riverine environments. Therefore, this paper evaluates the application of a supervised classification using ENVI’s Support Vector Machine and an object based classification using the ArcGIS extension Feature Analyst to extract river features from Landsat 5 TM images including ancillary data files. Furthermore, the results of the classification methods are evaluated with regard to thematic correctness and separability of the various classified river features using accuracy assessment as presented in the specialist literature. Finally the long-time changes in the riverine environments are traced by interpreting the distribution of the classified river features. Accordingly, the approach of this work contributes to on-going research concerning semiautomatic or automatic river feature extraction