Inferring spectral signatures from ground based natural images has acquired a lot of interest in applied deep learning. In contrast to the spectra of ground based images, aerial spectral images have low spatial resolution and suffer from higher noise interference. In this paper, we train a conditional adversarial network to learn an inverse mapping from a trichromatic space to 31 spectral bands...

Remotely sensed data are essentially used for land cover and vegetation classification. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. One of the most common attempts to improve image classification is the integration of ancillary data into classification. In this study, an approach for integrating topographic data into land cove...

Techniques based on multi-temporal, multi-spectral and satellitesensor have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes. Multi-temporal images processing becomes more and more important in monitoring earth surface. The large collection of past and present remote sensing imagery makes it possible to analyze spatio-temporal and spectrotemporal pattern ...

We introduce spectral Global Intrinsic Symmetry Invariant Functions (GISIFs), a class of GISIFs obtained via eigendecomposition of the Laplace-Beltrami operator on compact Riemannian manifolds, and provide associated theoretical analysis. We also discretize the spectral GISIFs for 2D manifolds approximated either by triangle meshes or point clouds. In contrast to GISIFs obtained from geodesic d...