Mr. David R. Sloggett Profile

David R. Sloggett

Principal Consultant at LogicaCMG

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Publications (3)

Proceedings Article | 28 October 2022 Presentation + Paper

Meaningful machine learning robustness evaluation in real-world machine learning enabled system contexts

Ben Hiett, Peter Boyd, Charles Fletcher, Sam Gowland, James Sharp, David Slogget, Alec Banks

Proceedings Volume 12276, 1227608 (2022) https://doi.org/10.1117/12.2638492

KEYWORDS: Performance modeling, Data modeling, Machine learning, Image quality, Visualization

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Proceedings Article | 21 December 1994 Paper

EARSEC SAR processing system

Mark Protheroe, David Sloggett, Alois Sieber

Proceedings Volume 2316, (1994) https://doi.org/10.1117/12.197552

KEYWORDS: Synthetic aperture radar, Data processing, Doppler effect, Sensors, Calibration, Polarimetry, Remote sensing, Antennas, Image processing, Control systems

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Traditionally, the production of high quality Synthetic Aperture Radar imagery has been an area where a potential user would have to expend large amounts of money in either the bespoke development of a processing chain dedicated to his requirements or in the purchase of a dedicated hardware platform adapted using accelerator boards and enhanced memory management. Whichever option the user adopted there were limitations based on the desire for a realistic throughput in data load and time. The user had a choice, made early in the purchase, for either a system that adopted innovative algorithmic manipulation, to limit the processing time of the purchase of expensive hardware. The former limits the quality of the product, while the latter excludes the user from any visibility into the processing chain. Clearly there was a need for a SAR processing architecture that gave the user a choice into the methodology to be adopted for a particular processing sequence, allowing him to decide on either a quick (lower quality) product or a detailed slower (high quality) product, without having to change the algorithmic base of his processor or the hardware platform. The European Commission, through the Advanced Techniques unit of the Joint Research Centre (JRC) Institute for Remote Sensing at Ispra in Italy, realizing the limitations on current processing abilities, initiated its own program to build airborne SAR and Electro-Optical (EO) sensor systems. This program is called the European Airborne Remote Sensing Capabilities (EARSEC) program. This paper describes the processing system developed for the airborne SAR sensor system. The paper considers the requirements for the system and the design of the EARSEC Airborne SAR Processing System. It highlights the development of an open SAR processing architecture where users have full access to intermediate products that arise from each of the major processing stages. It also describes the main processing stages in the overall architecture and illustrates the results of each of the key stages in the processor.

Proceedings Article | 17 August 1994 Paper

Approach to the automated identification of forests and forest change in remotely sensed images

W. Newton, C. Gurney, David Sloggett, Ian Dowman

Proceedings Volume 2357, (1994) https://doi.org/10.1117/12.182812

KEYWORDS: Algorithm development, Detection and tracking algorithms, Forestry, Edge detection, Image classification, Commercial off the shelf technology, Raster graphics, Photogrammetry, Detector development, Analytical research

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