HPC MSU

Publication Abstract

Application of Omni-directional Texture Analysis to SAR Images for Levee Landslide Detection

Lee, M., Aanstoos, J.V., Bruce, L.M., & Prasad, S. (2012). Application of Omni-directional Texture Analysis to SAR Images for Levee Landslide Detection. IEEE Geoscience and Remote Sensing Society IGARSS 2012. Munich, Germany: IEEE.

Abstract

Application of Omni-Directional Texture Analysis to SAR Images for Levee Landslide Detection Matthew A. Lee, James V. Aanstoos, Lori M. Bruce, and Saurabh Prasad Introduction Earthen Levees protect large areas of populated and cultivated land in the United States from flooding. In the United States there are more than 150,000 kilometers of levee structures of varying designs and conditions. The potential loss of life and property associated with the catastrophic failure of levees can be extremely large [1]. Currently, there are limited processes in place to prioritize the monitoring of large numbers of dam and levee structures. There is a need to prioritize the monitoring of the network of dam and levee structures. Levee managers and federal agencies will benefit from any tools allowing them to assess levee health rapidly with robust techniques that identify, classify and prioritize levee vulnerabilities with lower costs than traditional programs not based on the use of remote sensing. This paper explores different types of gray level co-occurrence matrix (GLCM) [2] texture features which can be used to identify landslides on levees using Synthetic Aperture Radar (SAR).