Tag: synthetic aperture sonar

Master’s Defenses!

Congratulations to our labmates, Hudanyun Sheng and Princess Lyons, for successful Master’s defenses!¬†¬† Hudanyun conducted work on “Switchgrass Genotype Classification using Hyperspectral Imagery”, while Princess investigated¬† “Anomaly and Target Detection in Synthetic Aperture Sonar”. Great job, you two!

Evaluation of image features for discriminating targets from false positives in synthetic aperture sonar imagery

Abstract: With the increasing popularity of using autonomous underwater vehicles (AUVs) to gather large quantities of Synthetic Aperture Sonar (SAS) seafloor imagery, the burden on human operators to identify targets in these seafloor images has increased significantly. Existing methods of… Read More

Deep convolutional neural network target classification for underwater synthetic aperture sonar imagery

Abstract: In underwater synthetic aperture sonar (SAS) imagery, there is a need for accurate target recognition algorithms. Automated detection of underwater objects has many applications, not the least of which being the safe extraction of dangerous explosives. In this paper,… Read More

Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach

Abstract: Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose… Read More

Comparison of Prescreening Algorithms for Target Detection in Synthetic Aperture Sonar Imagery

Abstract: Automated anomaly and target detection are commonly used as a prescreening step within a larger target detection and target classification framework to find regions of interest for further analysis. A number of anomaly and target detection algorithms have been… Read More

Multiple-instance learning-based sonar image classification

Abstract: An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the… Read More