ON LAND SLIDE DETECTION USING TERRASAR-X 
OVER EARTHEN LEVEES 
M. Mahrooghy * *, J. Aanstoos *, S. Prasad ", N. H. Younan* 
* Geosystems Research Institute, Mississippi State University, Mississippi, MS 39762, USA - (majid, 
aanstoos)@gri.msstate.edu 
* Dept. of Electrical and Computer Engineering, Mississippi State University, Mississippi, MS 39762, USA - 
younan@ece.msstate.edu 
? Dept. of Electrical and Computer Engineering, University of Houston, Houston, Texas 77004, USA 
sprasad2@uh.edu 
KEY WORDS: 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. À 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. 
1. INTRODUCTION 
Earthen levees have an important role to protect large areas of 
inhabited and cultivated land in the US from flooding. A failure 
of the levees can threaten the loss of life and property. There are 
more than 150,000 kilometres of levee structure with different 
designs and conditions over the entire US. Therefore, 
monitoring the levee system in order to detect and classify the 
levee vulnerabilities can help levee boards and federal agencies 
to repair them rapidly with lower costs than traditional methods 
which can cost many millions of dollars (Aanstoos, 2010). One 
of the problems which can lead to a complete failure during a 
high water event is a slough slide. Slough slides are slope 
failures along a levee. A slough slide leaves areas of the levee 
vulnerable to seepage and failure during high water events. The 
roughness and corresponding textural characteristics of the soil 
in a slide can change the amount and pattern of radar 
backscatter (Aanstoos, 2011). The reasons that a slide occurs 
are studied in Hossain et al. (2006). 
Early detection of slide events can assist levee boards in 
beginning their efforts to fix and repair the problems and 
prevent more costly damage. One efficient and cost effective 
way to detect these vulnerabilities is to use remote sensing, 
which is more effective than frequent site visits. 
In addition, the type of vegetation that grows in a slide area 
differs from the surrounding levee vegetation, which can also be 
utilized in detecting slides (Hossain, 2006). Also, since there is 
a relationship between the SAR backscatter and soil moisture 
(Oh, 2004), SAR images can be used to monitor the soil 
  
* Corresponding author 
moisture for detecting the slides around levees (Mahrooghy, 
2011). 
Other remote sensing based methods of detecting landslides 
have utilized digital elevation models (Tsutsui et. al. 2007 and 
McKean et. al,, 2004) and two-pass differential interferometry 
based on SAR images from RADARSAT-1 (Bulmer et. al, 
2006). 
In this paper, an algorithm based on a neural network is 
developed to detect landslides on levees from single-pass 
polarimetric SAR. The paper is organized as follows: the data 
used in this study is explained in section 2. In section 3, the 
methodology and block diagram of the algorithm is described. 
The result of applying the algorithm in the area of study is 
discussed in the results and validation section, and finally, a 
conclusion and summary of this study is provided in section 5. 
2. DATA 
Our area of study is part of the levee system along the lower 
Mississippi River and the western boundary of the state of 
Mississippi. Two parts of the study area (about a 1 Km stretch 
of levees) in this region are studied for algorithm training and 
testing. Radar imagery from the German TerraSAR-X satellite 
was acquired over the study area. TerraSAR-X is a SAR sensor 
imaging at 9.65 GHz with variable incidence angle and ground 
resolution. It acquires quad polarized images in experimental 
mode and single and dual polarized HH/VV in other situations. 
The TerraSAR-X data used for this study was acquired on 
September 04, 2010 with 1.5 m ground resolution. The image is 
  
   
   
    
  
  
  
  
   
  
   
   
   
  
    
  
  
  
  
  
  
  
   
   
    
    
   
   
   
    
   
   
   
   
   
   
   
    
  
   
   
   
   
   
    
    
   
   
    
  
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