A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers

Optimized Spectral Transformation for Detection and Classification of Buried Radioactive Waste -11310

ABSTRACT We investigate detection and classification of buried radioactive materials of interest using data collected by a Sodium Iodide (NaI) detector with short sensor dwell time (i.e., less than or equal to 1s). The objective of detection is to detect a target from background or non-target materials, while the objective of classification is to classify targets buried at different depths. Three spectral transforms using binned energy windows can help alleviate the negative impact from background and suppress trivial spectral variation. However, their performance is sensitive to bin partition parameters including the number of bins and their bin-widths. We have developed a particle swarm optimization (PSO)-based automatic algorithm to determine these parameters. In this paper, we propose to apply multi-objective PSO to optimize both the detection and classification accuracy simultaneously. The experimental results demonstrate that the multi-objective PSO can achieve the balance between these two objectives, and it may provide even better individual performance than a single-objective PSO

International AIDS Society global scientific strategy: towards an HIV cure 2016

Antiretroviral therapy is not curative. Given the challenges in providing lifelong therapy to a global population of more than 35 million people living with HIV, there is intense interest in developing a cure for HIV infection. The International AIDS Society convened a group of international experts to develop a scientific strategy for research towards an HIV cure. This Perspective summarizes the group's strategy

Radar and Radio Signal Processing

Radar is a technology used in many aspects of modern life, with many diverse civilian and military applications.[...

Joint Estimation of Doppler Stretch and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Sigmoid-FRFT Transform under the Impulsive Noise Environment

To overcome the limitation of performance degradation of existing methods based on fractional Fourier transform in impulsive noise, and fractional lower-order statistics based method dependence on a priori knowledge of the noise, a novel Sigmoid fractional Fourier transform (Sigmoid-FRFT) is presented in this paper. This novel approach is then used to estimate the Doppler stretch and time delay. Furthermore, the properties of the Sigmoid transform, robustness and boundedness of the Sigmoid-FRFT to the S α S noise, and the computation complexity of the Sigmoid-FRFT method are presented to evaluate the performance of the proposed method. Simulation results and theoretical analysis are presented to demonstrate the applicability of the forgoing method. It is shown that the proposed method not only can effectively suppress impulsive noise interference but also does not need a priori knowledge of the noise, with higher estimation accuracy and lower computational complexity in impulsive noise environments

A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments

In this paper, a novel method, that employs a fractional Fourier transform and a tuneable Sigmoid transform, is proposed, in order to estimate the Doppler stretch and time delay of wideband echoes for a linear frequency modulation (LFM) pulse radar in an alpha-stable distribution noise environment. Two novel functions, a tuneable Sigmoid fractional correlation function (TS-FC) and a tuneable Sigmoid fractional power spectrum density (TS-FPSD), are presented in this paper. The novel algorithm based on the TS-FPSD is then proposed to estimate the Doppler stretch and the time delay. Then, the derivation of unbiasedness and consistency is presented. Furthermore, the boundness of the TS-FPSD to the symmetric alpha stable ( S α S ) noise, the parameter selection of the TS-FPSD, and the feasibility analysis of the TS-FPSD, are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation is derived and computed in closed form, which shows that better performance has been achieved. Simulation results and theoretical analysis are presented, to demonstrate the applicability of the forgoing method. It is shown that the proposed method can not only effectively suppress impulsive noise interference, but it also does not need a priori knowledge of the noise with higher estimation accuracy in alpha-stable distribution noise environments

Parameter Estimation Based on Sigmoid Transform in Wideband Bistatic MIMO Radar System under Impulsive Noise Environment

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based on Sigmoid transform was used to estimate target parameters, which do not need a priori knowledge of the noise in an impulsive noise environment. Firstly, a novel wideband ambiguity function, termed Sigmoid wideband ambiguity function (Sigmoid-WBAF), is proposed to estimate the Doppler stretch and time delay by searching the peak of the Sigmoid-WBAF. A novel Sigmoid correlation function is proposed. Furthermore, a new MUSIC algorithm based on the Sigmoid correlation function (Sigmoid-MUSIC) is proposed to estimate the direction-of-departure (DOD) and direction-of-arrival (DOA). Then, the boundness of the Sigmoid-WBAF to the symmetric alpha stable () noise, the feasibility analysis of the Sigmoid-WBAF, and complexity analysis of the Sigmoid-WBAF and Sigmoid-MUSIC are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation was derived and computed in closed form, which shows that better performance was achieved. Simulation results and theoretical analyses are presented to verify the effectiveness of the proposed method

Detection of Anomalies On Earthen Levees With and Without Feature Extraction Using Synthetic Aperture Radar Imagery

Early detection of anomalies on earthen levees by a remote sensing approach could save time and cost versus direct assessment. In this paper, we implemented the support vector machine (svm) supervised classification algorithm with and without feature extraction. Features were extracted using grey level co-occurrence matrix (glcm) features using phase and magnitude imagery of polarimetric synthetic aperture radar (polsar) for the identification of anomalies on levees. The effectiveness of the algorithms is demonstrated using fully quad-polarimetric l-band synthetic aperture radar (sar) imagery from the nasa jet propulsion laboratory\u27s (jpl\u27s) uninhabited aerial vehicle synthetic aperture radar (uavsar). The study area is a section of the lower mississippi river valley in the southern usa, where the us army corps of engineers maintains earthen flood control levees