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Abstract
Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWIR) images are analyzed separately and then combined for urban mapping in this study. The framework of its methodology is based on a two-level classification approach. In the first level, contributions of these two data sources in urban mapping are examined extensively by four types of classifications, i.e. spectral-based, spectral-spatial-based, joint classification, and multiple feature classification. In the second level, an objected-based approach is applied to decline the boundaries. The specificity of our proposed framework not only lies in the combination of two different images, but also the exploration of the LWIR image as one complementary spectral information for urban mapping. To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.
Acknowledgment
The authors would like to thank Telops Inc. (Quebec, Canada) for acquiring and providing the data used in this study, the IEEE GRSS Image Analysis and Data Fusion Technical Committee and Dr. Michal Shimoni (Signal and Image Centre, Royal Military Academy, Belgium) for organizing the 2014 Data Fusion Contest, the Centre de Recherche Public Gabriel Lippmann (CRPGL, Luxembourg) and Dr. Martin Schlerf (CRPGL) for their contribution of the Hyper-Cam LWIR sensor, and Dr. Michaela De Martino (University of Genoa, Italy) for her contribution to data preparation.