LAUSR

ABSTRACT Timely and detailed information on the situation in conflict areas is essential to monitor the impact of conflicts on civilians and to document human rights issues, such as large scale displacement. Remote sensing provides valuable means for conflict monitoring, especially in areas where the ground-based documentation of violence is hampered, e.g. by the limited access to or persistent insecurity of conflict zones. The manual analysis of remote-sensing data is time consuming and labour intensive, but automatic methods can increase the efficiency of corresponding workflows, if the required user interference is minimized. In this study, the use of object-based change analysis for the automatic and selective detection of destructed dwellings in bi-temporal images is explored in test areas in Darfur. The presented approach automatically determines areas of interest (settlements), and detects changes in those areas by analysis of two change features (change of edge intensity and spectral change). It applies automatically defined local reference values and thresholds of these change features to reduce the required user interference. In addition, the extended feature space in the object-based approach (including, e.g. shape, size, and relational features in addition to spectral properties) is used to distinguish destructed dwellings from other, similarly changed objects. The developed method was applied to two study areas using images from three different sensors (GeoEye-1, WorldView-2, and QuickBird) without adaptation of the thresholds or rule sets. This resulted in a producer’s accuracy of 75.4% in the first and 81.2% in the second study area. The achieved user’s accuracy was 73.3% in study area 1 and 77.2% in study area 2. The evaluation of the results shows that the automatic calculation of local reference values and thresholds for the change detection can increase robustness when the proposed method is applied on study areas with different image properties. It also demonstrates the advantages as well as the specific constraints of using object- and context-specific features in this use case for the extraction of a certain structure type on a high level of detail.