In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of
individual data. Complementary physical contents of data sources allow comprehensive and
precise information retrieve. Future applications will have many options on data sources. Such
privilege can be beneficial only if algorithms are ready to work with various data sources. However,
current data fusion studies mostly focus on the fusion of two data sources. Thus, we provide the
community a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS
includes synthetic aperture radar (SAR) data, multispectral image, hyperspectral image, digital
surface model (DSM), and geographic information system (GIS) data. All these data are collected
on the same date, 7th May 2018. MDAS is a new benchmark data set that provides researchers
rich options on data selections. We run experiments for three typical remote sensing applications,
namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data
set. Our experiments demonstrate the performance of representative state-of-the-art algorithms
whose outcomes can sever as baselines for further studies
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In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of
individual data. Complementary physical contents of data sources allow comprehensive and
precise information retrieve. Future applications will have many options on data sources. Such
privilege can be beneficial only if algorithms are ready to work with various data sources. However,
current data fusion studies mostly focus on the fusion of two data sources. Thus, we provide the
community a benc...
»
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