Volume II-3/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 1-6, 2013
https://doi.org/10.5194/isprsannals-II-3-W1-1-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 1-6, 2013
https://doi.org/10.5194/isprsannals-II-3-W1-1-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 May 2013

16 May 2013

LAND COVER CLASSIFICATION OF SATELLITE IMAGES USING CONTEXTUAL INFORMATION

B. Fröhlich3,1, E. Bach1, I. Walde3,2, S. Hese3,2, C. Schmullius3,2, and J. Denzler3,1 B. Fröhlich et al.
  • 1Computer Vision Group, Friedrich Schiller University Jena, Germany
  • 2Department of Earth Observation, Friedrich Schiller University Jena, Germany
  • 3Graduate School on Image Processing and Image Interpretation, ProExzellenz Thuringia, Germany

Keywords: Land Cover, Classification, Segmentation, Learning, Urban, Contextual

Abstract. This paper presents a method for the classification of satellite images into multiple predefined land cover classes. The proposed approach results in a fully automatic segmentation and classification of each pixel, using a small amount of training data. Therefore, semantic segmentation techniques are used, which are already successful applied to other computer vision tasks like facade recognition. We explain some simple modifications made to the method for the adaption of remote sensing data. Besides local features, the proposed method also includes contextual properties of multiple classes. Our method is flexible and can be extended for any amount of channels and combinations of those. Furthermore, it is possible to adapt the approach to several scenarios, different image scales, or other earth observation applications, using spatially resolved data. However, the focus of the current work is on high resolution satellite images of urban areas. Experiments on a QuickBird-image and LiDAR data of the city of Rostock show the flexibility of the method. A significant better accuracy can be achieved using contextual features.