2014 IEEE GRSS Data Fusion Contest
Multiresolution Fusion of Thermal Infrared Hyperspectral and VIS Data
The 2014 Data Fusion Contest is starting!
The 2014 Data Fusion Contest, organized by the Image Analysis and Data Fusion (IADF) Technical Committee of the IEEE Geoscience and Remote Sensing Society, aims at providing a challenging image analysis opportunity, including multiresolution and multisensor fusion, very high resolution imagery, and a completely new data type, which was never before considered in previous Data Fusion Contests.
The 2014 Contest involves two datasets acquired at different spectral ranges and spatial resolutions: a coarser-resolution long-wave infrared (LWIR, thermal infrared) hyperspectral data set and fine-resolution data acquired in the visible (VIS) wavelength range. The former is acquired by an 84-channel imager that covers the wavelengths between 7.8 to 11.5 µm with approximately 1-meter spatial resolution. The latter is a series of color images acquired during separate flight-lines with approximately 20-cm spatial resolution. The two data sources cover an urban area near Thetford Mines in Québec, Canada, and were acquired and were provided for the Contest by Telops Inc. (Canada).
The 2014 Data Fusion Contest consists of two parallel competitions:
(a) The CLASSIFICATION CONTEST, which is designed to promote innovation in classification algorithms, as well as to provide objective and fair comparisons among methods. The goal will be to exploit coarser resolution LWIR data and finer resolution VIS data to generate an accurate classification result at the finer of the two observed resolutions. Ranking will be based on quantitative accuracy parameters computed with respect to undisclosed test samples. In addition to accuracy, another relevant aspect to assess a classification method is its computational burden, given the provided amount of training samples. In the Classification Contest, participants will be given a limited time to submit their classification maps after the competition is started. To also allow participants to effectively focus their methods on the proposed multiresolution task and type of input data, the Classification Contest consists of two steps:
- Step 1: Participants are provided with a subset of the data, including ground truth to train their algorithms.
- Step 2: Participants will receive the full data set and will be asked to submit their classification maps in a short time. In parallel, they will submit a description of the approach used.
(b) The PAPER CONTEST, which aims at promoting novel synergetic uses of multiresolution and multisensor data. Participants will submit 4-page IEEE-style manuscripts using the aforementioned data for fusion tasks. Each manuscript will describe the addressed problem, the proposed method, and the experimental results. The topic of the manuscript in the data fusion area is totally open, and participants are encouraged to tackle open problems in multisensor and/or multiresolution data processing, as well as in the analysis of thermal hyperspectral imagery. Papers will be evaluated and ranked by an Award Committee.
Please visit the Contest website at the following URL for all information about the schedule for the two competitions, how to register, access the data, and participate, and about awards and prizes:
The Contest is being organized in collaboration with Dr. Michal Shimoni (Signal and Image Centre, Royal Military Academy, Belgium).
The IADF Technical Committee wish to express its greatest appreciation to Dr. Shimoni for her indispensable contribution to the organization of the contest, to Telops Inc. (Québec, Canada) for acquiring and providing the data used in both competitions, to the Centre de Recherche Public Gabriel Lippmann (CRPGL, Luxembourg) and to Dr. Martin Schlerf (CRPGL) for their contribution of the Hyper-Cam LWIR sensor, to Dr. Michaela De Martino (University of Genoa, Italy) for her contribution to the preparation of the Classification Contest, and to the IEEE GRSS for continuously supporting the annual Data Fusion Contest through funding and resources.