Search

In this article, an active millimeter-wave imager based on multiple-input multiple-output synthetic aperture radar (MIMO-SAR) scheme is presented. In order to achieve two-dimensional (2-D) synthetic aperture, a one-dimensional (1-D) high-precision mechanical scanning frame is used to combine with a self-designed 1-D millimeter-wave sparse MIMO array. The hardware composition of the presented imager is introduced in detail. In addition, a new operation method of transmitting and receiving based on the designed 1-D millimeter-wave sparse MIMO array is proposed. Different from the full-channel mode, a minimum number of transmitters and receivers is required in our method to make the equivalent phase centers (EPCs) evenly distributed while covering the whole array aperture. In this way, the amount of the echo data can be significantly reduced, and the real-time imaging can be realized by applying fast imaging algorithms. Experimental results show that the proposed imager and the corresponding array operation method perform well in high-resolution imaging and concealed object detection. [ABSTRACT FROM AUTHOR]

The main focus of this research is to detect vulnerabilities on the Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) imagery. Unstable slope conditions can lead to slump slides, which weaken the levees and increase the likelihood of failure during floods. On-site inspection of levees is expensive and time-consuming, so there is a need to develop efficient automated techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. Synthetic Aperture Radar technology, due to its high spatial resolution and potential soil penetration capability, is a good choice to identify problem areas along the levee so that they can be treated to avoid possible catastrophic failure. This research analyzes the ability of detecting the slump slides on the levee with NASA JPL's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data. The main contribution of this research is the development of a machine learning framework to (1) provide improved knowledge on the status of the levees, (2) detect anomalies on the levee sections, (3) provide early warning of impending levee failures, and (4) develop efficient tools for levee health assessment. Textural features have been computed and utilized in the classification tasks to achieve efficient levee characterization. The RX anomaly detector, a training-free unsupervised classification algorithm, detected the active slides on the levee at the time of image acquisition and also flagged some areas as 'anomalous,' where new slides appeared at a later date. [ABSTRACT FROM AUTHOR]