Context and objectives
to develop geometric activity indices as an alternative for more traditional texture indices that better describe the geometry of man-made structures in VHR imagery;
to propose a strategy for selecting the most suitable geometric activity indices to be included in a per-pixel artificial network classification framework for identifying a variety of man-made structures from VHR data;
to propose a method for automatic vectorization, starting from the results of a soft classification of VHR data, that can produce data that better fulfill the needs of potential users of the data;
to produce prototype software routines for the implementation of the proposed computer vision methods
Expected scientific results
Because the current state-of-the-art corner detectors produce very high false detection rates, results produced by these detectors proved to be unsuitable for deriving meaningful geometric activity indices for man-made object classification. For deriving a final set of geometric activity indices attention was therefore focused on information provided by ridge detectors and morphological operators. Several ridge based features were defined giving a per-pixel indication of the presence of linear structures. The main problem with this kind of features, however, is the unreliability near borders. Therefore, from initial classification experiments with spectral and ridge features, only little improvement was achieved in distinguishing between roads and other man-made objects (buildings in particular). Since morphological features are less affected by these problems and contain similar information, much attention was given to the development of morphologically-based indices. Methods were developed to derive morphological signatures giving an indication of the minimum and maximum size of an object at different scale levels. The combined use of ridge features and morphological features led to significant improvements in the classification accuracies of man-made object classes, especially for roads. Compared to a scenario where only spectral information is used, accuracy improvements of 12 to 15% were observed. Although the use of object-based features generated by eCognition software also led to an improvement of classification accuracy for roads, the gain in accuracy was less than with geometric activity features. Combining the use of geometric activity features with object-based features leads to best results for building detection, although the accuracy for the most prominent building class remains low (around 60%).