Volume XLII-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 59-64, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-59-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 59-64, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-59-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

ON THE CATEGORIZATION OF HIGH ACTIVITY OBJECTS USING DIFFERENTIAL ATTRIBUTE PROFILES

M. Boldt1, A. Thiele1,2, K. Schulz1, and S. Hinz2 M. Boldt et al.
  • 1Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76275 Ettlingen, Germany
  • 2Institute of Photogrammetry and Remote Sensing IPF, Karlsruhe Institute of Technology KIT, 76128 Karlsruhe, Germany

Keywords: Synthetic Aperture Radar, SAR, TerraSAR-X, Time Series, Change Detection, Change Analysis, Clustering

Abstract. Change detection represents a broad field of research being on demand for different applications (e.g. disaster management and land use / land cover monitoring). Since the detection itself only delivers information about location and date of the change event, it is limited against approaches dealing with the category, type, or class of the change objects. In contrast to classification, categorization denotes a feature-based clustering of entities (here: change objects) without using any class catalogue information. Therefore, the extraction of suitable features has to be performed leading to a clear distinction of the resulting clusters.

In previous work, a change analysis workflow has been accomplished, which comprises both the detection, the categorization, and the classification of so-called high activity change objects extracted from a TerraSAR-X time series dataset. With focus on the features used in this study, the morphological differential attribute profiles (DAPs) turned out to be very promising. It was shown, that the DAP were essential for the construction of the principal components.

In this paper, this circumstance is considered. Moreover, a change categorization based only on different and complementary DAP features is performed. An assessment concerning the best suitable features is given.