The "Best Paper Challenge" award as the first author for the 2013 IEEE GRSS (Geoscience and Remote Sensing Society) Data Fusion Contest

My paper fusing hyperspectral and LiDAR remote sensing data for land-use classification won the “Best Paper Challenge” Award of the 2013 IEEE GRSS Data Fusion Contest from over 50 submissions from universities, national labs, and research institutes around the world.

The IEEE-GRSS Data Fusion Contest was established in 2006, by the IEEE Geoscience and Remote Sensing Society, to evaluate methodologies in the realm of multi-sensor data fusion. Data fusion is the combination of information from multiple sources and is used in many applications to increase system reliability, improve performance or obtain a more complete situational awareness picture.

This year’s Contest focused on exploiting airborne imagery of urban areas using hyperspectral and LiDAR (Light Detection and Ranging) data. Hyperspectral images are optical images that cover the visual and near-infrared spectral range. As opposed to images taken by a CCD (charge coupled device) camera which show just three colour channels, hyperspectral images acquire more than 100 bands. Contestants were provided with hyperspectral and LiDAR-derived data acquired over the University of Houston, USA, and the surrounding area. The scene covered an area of 4.2km2 and had a spatial resolution of 2.5 meters. The aim of the Contest was to label the data into 15 land cover and land use classes, going from healthy grass, stressed grass, synthetic grass, trees, road, highway, railway, commercial building, residential building, soil, water, tennis court, running track to parking lots.

Fabio Pacifici, Chair of the IEEE-GRSS Data Fusion Technical Committee, commented “In its seventh issue, the IEEE-GRSS Data Fusion Contest has become world renowned. We receive entries from around the world and their quality is extremely high,” More than 900 researchers from universities, national labs, space agencies, and corporations from across the globe entered the Contest, which is designed to promote novel synergetic use of hyperspectral and LiDAR data. A team from AGT International, Germany, won the first prize on the Best Classification Result.