The use of Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) approaches to build 3D models of structures belonging to the Cultural Heritage environment is becoming more widespread. Due to the big dimensions of the digital aerial images generated by airborne sensors and, consequently, computation problems, these images were rarely used for the construction of 3D models through SfM-MVS approach. In addition, the values of overlap used in traditional aerial surveys would lead to a geometry of acquisition that is quite weak. For these reasons, the use of aerial images generated by airborne sensors in the SfM-MVS approach was limited. However, taking into account the possibility to acquire aerial nadir and oblique images according to multi-view, the increasing of High Performance Computation and the use of Direct Georeferencing, the quality of the city model obtained with the SfM/MVS approach was evaluated on a dataset of aerial images involving the Old Town of Bordeux.
Abstract:In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multidate ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multidate ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area.
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