A random forest and superpixels approach to sharpen thermal infrared satellite imagery (Conference Presentation)
Thermal infrared (T IR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform. Often, T IR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen T IR imagery to shortwave band pixel resolutions. One of the most classic thermal sharpening technique is T sHARP . It uses a relationship between land surface temperature and normalized vegetation index (N DV I). However, there are several studies that prove that a single relationship between T IR and N DV I may only exist for a limited class of landscape. Our work hypothesis stated that it is possible to improve the spatial resolution of T IR imagery considering a relationship between vegetation and several soil spectral indexes and T IR as well the spatial context information. In this work, the potential of Superpixels (SP ) combined with Regression Random Forest (RRF ) is used to augmenting the spatial resolution of the Landsat 8 T IR (Band 10 and 11) imagery to their visible (V IS) spatial resolution. The SP allows to consider the contextual information over the land cover, and RF allows to integrate in a unique model the relationship between five spectral indices and T IR data. The results obtained by SP-RRF approach shows the potential of this methodology, compared with classical T sHARP method.