Dr. Martin Isenburg
Hands-on Course to LiDAR processing with LAStools
This course will include both theoretical and hands-on lectures on the LiDAR processing tools, which are widely known for their blazing speeds and high productivity. The software combines robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The LAStools software suite has deep market penetration and is heavily used in the commercial sector, government agencies, research labs, and educational institutions alike — filtering, tiling, rasterizing, triangulating, converting, clipping, quality-checking, etc.
More information can be found at: http://rapidlasso.com/2015/09/21/creating-dtms-from-dense-matched-points-of-uav-imagery-from-senseflys-ebee/
Dr. Harm Greidanus
Principles of Synthetic Aperture Radar imaging, and application to maritime scenarios and ship detection
Radar and Synthetic Aperture Radar (SAR) imaging; Maritime phenomena in SAR images.
Ship detection; Systems for maritime surveillance and their practical use.
Understanding the possibilities and limitations of today’s technologies for maritime surveillance, in particular the use of radar images
Radar, and in particular Synthetic Aperture Radar (SAR), is a powerful instrument for maritime surveillance, which has the purpose to be aware of what is happening at sea. SAR images over the sea show the presence of ships, but also show a host of physical maritime phenomena, such as wind, waves, fronts and even sea bottom effects. The operation of SAR from satellites gives global coverage with regular updates, and now that the EU’s Sentinel-1 satellite SAR makes its data available for free there is a huge body of data to work with. The lectures will cover: The principles of radar imaging in general and the specifics of Synthetic Aperture Radar; Maritime phenomena seen in SAR images and SAR imaging effects; Methodology for ship detection and classification in SAR images; Possibilities and limitations of satellite as a platform for SAR; Other available systems and data for maritime surveillance and integration between them. The participants will learn what is possible and what not with satellite SAR for maritime surveillance and ship detection, the current R&D issues, and how to access data.
Dr. Konstantinos Topouzelis
SAR Oil spill detection
This course will include theoretical lecture on the use of satellite SAR images for oil spill detection. It is organized in two parts: familiarization with the oil signature on SAR images and methodologies of detection. In the first part, there will be an introduction to the oil behaviour and the effects on the marine and coastal ecosystems, to the signature of oil spills from ship discharges, and to the way the natural phenomena are seen on SAR images.
In the second section, attendees will be informed on the different methodologies for identifying man made oil spill detection, their characteristic and limitations. The detection procedure will be analysed in terms of dark object isolation, feature extraction, statistical base comparison and finally decision/classification. Examples of spilling activities and lookalikes will be given and attendees will make a manual decision on the presence of oil spills. In the end, Integrated Maritime Services (IMS) will be discussed and examples of combining information from diverse sources will be given.
Dr. Konstantinos Topouzelis
Coastal Monitoring using UAV
The constant technological evolution in Computer Vision and Unmanned Aerial Vehicles (UAVs), as well as the miniaturization of sensors, may lead to surveys with high-quality spatial information and products for several applications. The use of UAV systems to costal monitoring will be presented in this course through dedicated examples.
Marine Litter detection: mapping and detecting the extent of the refugee arrival along the eastern coast of Lesvos island, Greece. The objectives of this preliminary study were to assess: (i) the extent of the refugee arrival related marine litter problem along the eastern coast of Lesvos; and (ii) the efficiency and cost-effectiveness of new technologies to provide quick, accurate and quantitative assessments of the marine litter distribution in order to plan efficiently the cleanup operations.
Coastal mapping: Coastal zones identification and 3D costal mapping. This study present methodologies for identifying coastline and coastal zones (coastal morphology). Object based image analysis used to create objects by grouping pixels that had the same spectral characteristics together and extract statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input.
Seagrass mapping: From ground truth to country scale mapping through local area and regional scale maps. The Common Fisheries Policy of EU requires mapping highly important habitats for fish production (e.g. underwater flowering meadows) in all EU Member States for the sustainable management of living aquatic resources and marine ecosystems. This is an example of seagrass mapping by aerial orthophoto and satellite images in several spatial scales.
Marine spatial planning: the use of high resolution spatial data towards the protection and conservation of biodiversity in the context of an integrated Marine Spatial Plan (MSP) in the Aegean Sea. This is an example on the use of UAV and satellite data for defining Marine Protected Areas (MPAs) and protection zones for the conservation of all-important and vulnerable habitats and species, as defined by national and community legislation and international agreements.
High Precision Survey of coastal areas: a flexible and attractive solution to produce accurate and high qualitative spatial data and geo visualizations. This is an example of significant contribution to cost effectiveness of monitoring the coastal area from stakeholder perspective.
Dr. Ribana Roscher
Unsupervised Learning for Remote Sensing Data
An unprecedented amount of remote sensing data is now available due to the growing number of satellites with embedded sensors characterized by increasing spatial and spectral resolution. The analysis of the data is often a prerequisite for a successful interpretation, and demands for unsupervised learning techniques. This course will introduce various techniques for unsupervised learning for remote sensing data. It covers the big data aspect of remote sensing data, classification paradigms exploiting unlabeled data, sparse representation and analysis techniques which are designed to deal with large-scale data.