Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 497-501, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-497-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 497-501, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-497-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

ON LAND SLIDE DETECTION USING TERRASAR-X OVER EARTHEN LEVEES

M. Mahrooghy1, J. Aanstoos1, S. Prasad2, and N. H. Younan3 M. Mahrooghy et al.
  • 1Geosystems Research Institute, Mississippi State University, Mississippi, MS 39762, USA
  • 2Dept. of Electrical and Computer Engineering, Mississippi State University, Mississippi, MS 39762, USA
  • 3Dept. of Electrical and Computer Engineering, University of Houston, Houston, Texas 77004, USA

Keywords: Hazards, Synthetic Aperture Radar (SAR), Feature Extraction, Neural Networks

Abstract. Earthen levees have an important role to protect large areas of inhabited and cultivated land in the US from flooding. Failure of the levees can threaten the loss of life and property. One of the problems which can lead to a complete failure during a high water event is a slough slide. In this research, we are trying to detect such slides using X-band SAR data. Our methodology consists of the following four steps: 1) segmentation of the levee area from background; 2) extracting features including backscatter features and texture features; 3) training a back propagation neural network classifier using ground-truth data; and 4) testing the area of interest and validation of the results using ground truth data. A dual-polarimetric X-band image is acquired from the German TerraSAR-X satellite. Ground-truth data include the slides and healthy area. The study area is an approximately 1 km stretch of levee along the lower Mississippi River in the United States. The output classification shows the two classes of healthy and slide areas. The results show classification accuracies of approximately 67% for detecting the slide pixels.