Volume II-3/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 143-148, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-143-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 143-148, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-143-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Mar 2015

11 Mar 2015

DISCRIMINATION OF URBAN SETTLEMENT TYPES BASED ON SPACE-BORNE SAR DATASETS AND A CONDITIONAL RANDOM FIELDS MODEL

T. Novack and U. Stilla T. Novack and U. Stilla
  • Photogrammetry and Remote Sensing, TU München, 80333 Munich, Germany

Keywords: Urban structures types, Synthetic Aperture Radar, Conditional Random Fields

Abstract. In this work we focused on the classification of Urban Settlement Types (USTs) based on two datasets from the TerraSAR-X satellite acquired at ascending and descending look directions. These data sets comprise the intensity, amplitude and coherence images from the ascending and descending datasets. In accordance to most official UST maps, the urban blocks of our study site were considered as the elements to be classified. The considered USTs classes in this paper are: Vegetated Areas, Single-Family Houses and Commercial and Residential Buildings. Three different groups of image attributes were utilized, namely: Relative Areas, Histogram of Oriented Gradients and geometrical and contextual attributes extracted from the nodes of a Max-Tree Morphological Profile. These image attributes were submitted to three powerful soft multi-class classification algorithms. In this way, each classifier output a membership value to each of the classes. This membership values were then treated as the potentials of the unary factors of a Conditional Random Fields (CRFs) model. The pairwise factors of the CRFs model were parameterised with a Potts function. The reclassification performed with the CRFs model enabled a slight increase of the classification’s accuracy from 76% to 79% out of 1926 urban blocks.