With the recent launch of several satellites with various modalities and resolutions, the huge amount of Earth observation remote sensing data has to be dealt with. For the same geographical area, satellite image time series, very high spatial resolution images, or hyperspectral images, are now easily available. Some of them, are freely available through open-access programs, such as Copernicus or the Landsat Open Archive. One of the most important applications of these remote sensing data is the classification of pixels, in terms of land cover, land use, or cover changes.
However, this massive flow of data, in terms of spatial coverage and temporal sampling, has the potential to develop applications at a global scale. However, conventional algorithms defined for small or moderate sized remote sensing images do not scale well. Hence, specific works are needed to fully exploit the data. In particular, the relevant information for the classification purposes might be hidden by the full set of features. Hence, feature extraction from the spectral, temporal and spatial dimensions is mandatory to provide detailed and accurate thematic maps.
This Special Issue will focus on state-of-the-art or newly-developed methods for the classification and feature extraction for remote sensing images. It will cover (but will not be limited to) the following topics: Spatial, temporal and spectral feature extraction, data-driven feature extraction, classification of multimodal remote sensing data for any thematic application (urban, agricultural or ecological ones) and for any scales, from locals to global ones.
Dr. Mathieu Fauvel
Dr. Jordi Inglada
Dr. Marco Chini
Dr. Fabio Pacifici
(c) GdR 720 ISIS - CNRS - 2011-2018.