The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 973–980, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 973–980, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022
 
30 May 2022
30 May 2022

INTEGRATION OF KALMAN FILTERING OF NEAR-CONTINUOUS SURFACE CHANGE TIME SERIES INTO THE EXTRACTION OF 4D OBJECTS-BY-CHANGE

K. Anders1, L. Winiwarter1, D. Schröder2,3, and B. Höfle1,4 K. Anders et al.
  • 13D Geospatial Data Processing Research Group (3DGeo), Institute of Geography, Heidelberg University, Germany
  • 2Department of Civil and Mining Engineering, DMT GmbH Co. KG, Essen, Germany
  • 3Faculty of Geoscience, Geotechnology and Mining, University of Mining and Technology Freiberg, Germany
  • 4Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany

Keywords: 4D change analysis, terrestrial laser scanning, change detection, geoscientific monitoring, uncertainty

Abstract. Automatic extraction of surface activity from near-continuous 3D time series is essential for geographic monitoring of natural scenes. Recent change analysis methods leverage the temporal domain to improve the detection in time and the spatial delineation of surface changes, which occur with highly variable spatial and temporal properties. 4D objects-by-change (4D-OBCs) are specifically designed to extract individual surface activities which may occur in the same area, both consecutively or simultaneously. In this paper, we investigate how the extraction of 4D-OBCs can improve by considering uncertainties associated to change magnitudes using Kalman filtering of surface change time series. Based on the change rate contained in the Kalman state vector, the method automatically detects timespans of accumulation and erosion processes. This renders change detection independent from a globally fixed minimum detectable change value. Considering uncertainties associated to change allows detecting and classifying more occurrences of relevant surface activity, depending on the change rate and magnitude. We compare the Kalman-based seed detection to a regression-based method using a three-month tri-hourly terrestrial laser scanning time series (763 epochs) acquired of mass movements at a high-mountain slope in Austria. The Kalman-based method successfully identifies all relevant changes at the example location for the extraction of 4D-OBCs, without requiring the definition of a global minimum change magnitude. In the future, we will further investigate which kind of change detection method is best suited for which types of surface activity.