The use of experiential learning in CS and related disciplines has long been explored. Experiential learning allows students to use hands-on, ''real world'' projects to help make cognitive connections between theory and practice. The increasing accessibility of virtual reality (VR) has opened new avenues for experiential student learning, offering the promise of building both hard and soft skills among students. VR also presents opportunities for interdisciplinary projects which are socially impactful and emotionally engaging. This poster presents an overview of a multi-year project to incorporate experiential VR development into a capstone experience for computing majors. The capstone experience focuses on the development, modification, and enhancement of anti-cyberbullying VR experiences for use in K-12 environments. These projects offer the opportunity for students to engage with community stakeholders and research teams to advance violence prevention programs. The poster will provide an overview of the project, the course pedagogy, and reflections and experiences from the first course offering. The poster will also describe the immersive experiences developed by students and the context of their ''real-world'' use.

This study presented an effective algorithm that measures target azimuth and range velocity of vessels in SAR image without assistance of AIS information. As the SAR Doppler history was modified from target velocity, azimuth and range velocity caused the target to be defocused and shifted, respectively. The proposed algorithm utilized phase refocusing function and sub-aperture analysis which calibrated the distortions and accordingly derived two-dimensional velocity of the target in a precise manner.

Along with vessel detection, vessel recognition in high-resolution SAR images was necessary in order to monitor marine vessels effectively. However, lack of target data and phase defocusing of target from its velocity limited the recognition performance, especially when using detectors based on artificial intelligence. This study accordingly proposed effective vessel recognition in high-resolution ICEYE spotlight SAR images consecutively utilizing (i) vessel detector robust to defocused moving vessels and (ii) mitigation of moving target phase distortion. In order to apply quantitative and qualitative training data enhancement, a target velocity SAR phase refocusing function was developed. The proposed target velocity SAR phase refocusing function generated defocused SLC image with respect to different target azimuth velocity, which can be utilized for both training data augmentation and refocusing of velocity-induced phase distortion. Achievement of stable vessel recognition performance was enabled from (i) robust vessel detection on defocused moving vessels and (ii) well-focused detected vessel targets, both of which were consecutively applied using the proposed target velocity SAR phase refocusing function. Vessel detection results demonstrated robust performance regardless of vessel motion and vessel recognition results significantly improved after phase refocusing, both of which were subject to quantitative and qualitative training data enhancement. Performance of the proposed algorithm was analyzed both in terms of phase focusing and velocity estimation. Refocusing performance outperformed that of conventional state-of-the-art autofocusing algorithm, modified Phase Gradient Autofocusing, while azimuth velocity estimation derived the average offset of 0.68 m/s, which was regarded more accurate than previous azimuth velocity estimators based on single-channel SAR image.

This study quantitatively measured vessel rotational motion from satellite SAR data using a target motion phase refocusing function. In contrast to horizontal linear motion of vessel, vessel rotational motion caused by ocean wave force was not able to be monitored from AIS information and accordingly rarely studied. Nevertheless, extensive vessel rotational motion was directly related to vessel motion hazard and potential of maritime accident, which was required to be measured in order to thoroughly analyze vessel movement. A target motion SAR phase refocusing function was proposed, which effectively measured vessel velocity, acceleration, and accordingly its rotational motion, conventionally named as yawing, pitching, and rolling motion. A total of 29 vessels whose velocity exceeding 1 m/s were selected from three different satellite SAR images and analyzed using the target motion SAR phase refocusing function. It precisely measured vessel velocity and acceleration, and subsequently derived horizontal and vertical vessel angular accelerations, which reorganized vessel yawing, pitching, and rolling motion. When compared with AIS-driven motion, estimated azimuth velocity and horizontally projected range acceleration respectively derived RMSE offset of 0.49 m/s and 0.0032 m/s2. Moreover, the proposed phase refocusing function outperformed the conventional SAR phase focusing algorithms in aspect of focusing performance. As the measurement of the vessel rotational motion using the proposed refocusing function was presented, it can be practically applied to monitoring vessel motion hazard in ocean using satellite SAR data.

This study focuses on improving the geometric accuracy of small satellite Synthetic Aperture Radar (SAR) images through software techniques. Small satellites are increasingly being launched in the new space era, using cost-effective components like Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU). However, the resulting satellite images may have relatively poor quality. Geometric accuracy is crucial for satellite images, influenced by sensor acquisition geometry. While ground control points (GCPs) are commonly used for corrections, accurate calibration for ocean SAR images is limited. This study proposes using Automatic Identification System (AIS) data to interpolate ship positions and employing deep learning technology for ship detection, repeatedly matching AIS data with SAR images to enhance geometric accuracy. The effectiveness of this technique is evaluated using HiSEA-1 SAR images and demonstrated improvement is observed with an increasing number of ships.

As Doppler rate in azimuth compression of SAR image was estimated from immutable targets, moving objects in image coverage inevitably confronted azimuth defocusing. A phase compensation filter from target azimuth velocity was implemented to ICEYE SAR SLC images, optimized by minimizing Entropy. Conventional object detection algorithm based on artificial intelligence demonstrated the amelioration of detection performance in the refocused SAR images.

Rising relative sea level changes due to the ground subsidence in the coast can cause the risk of flooding in the low-lying areas. In this study, we estimated the subsidence along the coast for 50km length using time-series SAR Interferometry technique by acquiring Sentinel-1 SAR data of 78 scenes in descending and 81 scenes in ascending mode. Also, subsidence analysis at critical infrastructures such as Busan new port and Gimhae airport were conducted for analyzing the risk of flooding due to rapid subsidence.

Mitigation of geometric calibration offset in ocean without utilizing ground control points was investigated in this study. Real-time AIS information on vessels was exploited after preprocessing and accordingly tested against the detected vessels in the SAR image. Repetitive procedure of measuring the offset between the AIS sensor and the vessel detection was conducted and derived the SAR image of which the positioning offset was ameliorated. The proposed geo-location enhancement algorithm demonstrated the possibility of application in real-time vessel monitoring from remote sensing.

Real-time surveillance of vessel in ocean was previously enforced by AIS sensors, while the importance of weather independent monitoring apparatus, SAR, is currently being implemented. For practical application of SAR image based vessel monitoring however, correction of azimuth shift caused by the velocity of moving target was necessary. This study presented azimuth shift compensation on vessels detected from Cosmo-SkyMed SAR images using machine learning based object detector. Conventional azimuth velocity measurement using Doppler frequency estimation was implemented and transformed into azimuth shift. AIS information corresponding to SAR images was preprocessed in order to be employed as a reference. From three Cosmo-SkyMed SAR images, azimuth velocity estimation derived the average offset of 1.65 m/s, while azimuth shift measurement derived that of 70.69 m with respect to AIS information. Ensuing application of this study includes moving target indication especially in ground regions.

Monitoring and detecting ships via machine learning based algorithm were regarded efficient in martial and economic manners. As an algorithm regarding automated training data retrieval from SAR image was proposed, the identification of unclassified ships without AIS information could be raised as another challenging issue of ship surveillance. This study presented the effective identification algorithm of discerning unclassified ships from AIS information and the results of conventional ship detection based on machine learning. The accurately detected ships were selected from the conventional ship detection results, followed by the preprocessing of AIS information corresponding to the SAR images containing the detection results. Superposition of AIS information on accurate detection results was conducted and concluded the ships without AIS information as unclassified ships. From 3 Sentinel-1 SAR images, it obtained the average rate of identification as 85.67%. Additional research implementing the identification algorithm accompanied by rapidly acquired satellite or airborne SAR images could be effective in rendering a ship surveillance system with rapid response.

Ship detection in coastal regions accompanied by machine learning could be effective in economic and martial issues. However, conventional researches on ship detection mainly focused on modifying the training model itself instead of obtaining qualified training data. In order to ameliorate the training data in the aspect of quality and quantity, this research aims to directly obtain training data from AIS information. From discrete AIS information, interpolation on SAR acquisition time was conducted, followed by adjusting the Doppler frequency shift caused by each vessel's velocity. Training data was constructed from the adjusted position using internal location of AIS sensor on each type of vessel. Extracted training data by the proposed algorithm from Sentinel-1 images was tested with CNN model. As the detection performance of the extracted training data exceeded that from visual interpretation, this study concluded that qualified training data could be extracted from the proposed algorithm.