Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 167-174, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 167-174, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION

W. Ouerghemmi1, A. Le Bris1, N. Chehata2, and C. Mallet1 W. Ouerghemmi et al.
  • 1Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 94160 Saint-Mandé, France
  • 2EA 4592 Géoressources & Environnement, Bordeaux-INP/Université Bordeaux Montaigne, France

Keywords: Decision fusion, Optimization, Graphical model, Hyperspectral, Multispectral, Urban classification

Abstract. Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in this study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related to class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The classification accuracy is improved up to 5 %, with comparison to the best single source classification accuracy.