Multi-polarized L-band Synthetic Aperture Radar is investigated for its potential to screen earthen levees for weak points. Various feature detection and classification algorithms are tested for this application, including both radiometric and textural methods such as grey-level co-occurrence matrix and wavelet features.
Abstract-Key results are presented of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors used are: (1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR capable of sub-meter ground sample distance; and (2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier. The study area is a stretch of 230 km of levees along the lower Mississippi River. The L-band measurements can penetrate vegetation and soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Various feature types and classification algorithms were applied to the polarimetry data in the project; this paper reports the results of using the Support Vector Machine (SVM) and back-propagation Artificial Neural Network (ANN) classifiers with a combination of the polarimetric backscatter magnitudes and texture features based on the wavelet transform. Ground reference data used to assess classifier performance is based on soil moisture measurements, soil sample tests, and on site visual inspections.
In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical onroad scenarios, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle's type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability) dataset and is publicly available at https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz.
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