@inproceedings{db1d8cb7ffa24025a71fca25416cc8d0,
title = "Levee anomaly detection using polarimetric synthetic aperture radar data",
abstract = "This research presents results of applying the NASA JPL's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect anomalies on earthen levees. Two types of problems / anomalies that occur along these levees which can be precursors to complete failure during a high water event are slough slides and sand boils. The study area encompasses a portion of levees of the lower Mississippi river in the United States. Supervised and unsupervised classification techniques have been employed to detect slough slides along the levee. RX detector, a training-free classification scheme is introduced to detect anomalies on the levee and the results are compared with the k-means clustering algorithm. Using the available ground truth data, a supervised kernel based classification technique using a Support Vector Machine (SVM) is applied for binary classification of slides on the levee versus the healthy levee and the performance is compared with a neural network classifier.",
keywords = "anomaly detection, image classification, neural network classifier, RX detector, support vector machine, Synthetic Aperture Radar (SAR)",
author = "Lalitha Dabbiru and Aanstoos, \{James V.\} and Majid Mahrooghy and Wei Li and Arjun Shanker and Younan, \{Nicolas H.\}",
year = "2012",
doi = "10.1109/IGARSS.2012.6352460",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5113--5116",
booktitle = "IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
note = "32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 ; Conference date: 22-07-2012 Through 27-07-2012",
}