Tag: synthetic aperture sonar

EXPLAINABLE SAS ACCEPTED TO IGARSS!

Congratulations to our labmates: Sarah Walker, Joshua Peeples, Jeff Dale, James Keller and Alina Zare!  Their paper, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their… Read More

WEAKLY-LABELED RAND INDEX ACCEPTED TO IGARSS!

Congratulations to our labmates: Dylan Stewart, Anna Hampton, Alina Zare, Jeff Dale and James Keller!  Their paper, “The Weakly-Labeled Rand Index” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their paper, the authors introduce… Read More

EXPLAINABLE SYSTEMATIC ANALYSIS FOR SYNTHETIC APERTURE SONAR IMAGERY

Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar… Read More

THE WEAKLY-LABELED RAND INDEX

Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there… Read More

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