The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 409–414, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 409–414, 2013

  25 Sep 2013

25 Sep 2013


F. Tabib Mahmoudi1, F. Samadzadegan1, and P. Reinartz2 F. Tabib Mahmoudi et al.
  • 1Dept. of Surveying and Geomatics, College of Engineering, University of Tehran, Tehran, Iran
  • 2Dept. of Photogrammetry and Image Analysis, Remote Sensing Technology Institute, German Aerospace Centre (DLR), Oberpfaffenhofen 82234 Weßling, Germany

Keywords: Object Recognition, Decision Level Fusion, Visibility Map, Shadow Recovery, Texture, Weighting Strategy

Abstract. Spectral similarity and spatial adjacency between various kinds of objects, shadow and occluded areas behind high rise objects as well as complex relationships lead to object recognition difficulties and ambiguities in complex urban areas. Using new multi-angular satellite imagery, higher levels of analysis and developing a context aware system may improve object recognition results in these situations. In this paper, the capability of multi-angular satellite imagery is used in order to solve object recognition difficulties in complex urban areas based on decision level fusion of Object Based Image Analysis (OBIA). The proposed methodology has two main stages. In the first stage, object based image analysis is performed independently on each of the multi-angular images. Then, in the second stage, the initial classified regions of each individual multi-angular image are fused through a decision level fusion based on the definition of scene context. Evaluation of the capabilities of the proposed methodology is performed on multi-angular WorldView-2 satellite imagery over Rio de Janeiro (Brazil).The obtained results represent several advantages of multi-angular imagery with respect to a single shot dataset. Together with the capabilities of the proposed decision level fusion method, most of the object recognition difficulties and ambiguities are decreased and the overall accuracy and the kappa values are improved.