Application of omni-directional texture analysis to SAR images for levee landslide detection

This paper explores different types of gray level co-occurrence matrix (GLCM) [2] texture features for automated detection of landslides on levees using remotely sensed Synthetic Aperture Radar (SAR). Two approaches of texture analysis are investigated: one based on a rubber band straightening transform (RBST) which has been used extensively in the past in the medical imaging community, and one based on the authors´ developed approach of spiral straightening transform (SST). The transforms are used to project a circular region in the image to a rectangular representation where texture feature extraction can be applied. Straightforward linear discriminant analysis, for feature reduction and optimization, and maximum likelihood methods, for classification, are also utilized. The proposed system was tested on L-band SAR data with HH, HV, and VV polarizations collected from NASA´s UAVSAR of the Mississippi River levee system between Vicksburg, MS and Clarksdale, MS, USA. The proposed approach is shown to detect all known levee landslides in the test area with a low number of false positives.